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chemical_formula_reduced
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chemical_formula_anonymous
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nelements
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nsites
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cell
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pbc
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structure_hash
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multiplicity
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software
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method
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adsorption_energy
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atomic_forces
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atomization_energy
float64
cauchy_stress
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cauchy_stress_volume_normalized
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electronic_band_gap
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electronic_band_gap_type
string
energy
float64
formation_energy
float64
max_force_norm
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mean_force_norm
float64
property_object_metadata
string
property_object_metadata_id
string
property_object_last_modified
timestamp[ns]
property_object_hash
string
property_object_id
string
configuration_metadata
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configuration_metadata_id
string
configuration_labels
list
configuration_names
list
configuration_dataset_ids
list
configuration_last_modified
timestamp[ns]
configuration_hash
string
configuration_id
string
dataset_name
string
dataset_authors
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dataset_description
string
dataset_elements
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dataset_nelements
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dataset_nproperty_objects
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dataset_nconfigurations
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dataset_formation_energy_count
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dataset_nperiodic_dimensions
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string
dataset_total_elements_ratios
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dataset_extended_id
string
Ta
Ta
A
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VASP
DFT-PBE
null
[ [ 0, 0, 0 ] ]
null
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null
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MD_2876058403211599652222920
2024-08-16T15:30:32
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MD_7940789923608474251760237
null
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2023-12-02T06:25:21
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CO_1021767156492186795663389
Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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97,332.124136
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2023-12-02T01:26:31
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CC-BY-4.0
{'source-publication': 'https://doi.org/10.1103/PhysRevMaterials.4.093802', 'source-data': 'https://gitlab.com/acclab/gap-data/-/tree/master', 'other': None}
10.60732/43837a12
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DS_40zw467dnc6d_0
Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
Ta
Ta
A
[ 73 ]
[ "Ta" ]
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1
1
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VASP
DFT-PBE
null
[ [ 0, 0, 0 ] ]
null
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null
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MD_2876058403211599652222920
2024-08-16T15:18:38
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PO_1302022068679871736677211
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MD_3899010675055765347650092
null
[ "fcc" ]
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2023-12-02T06:25:21
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CO_1069934185405209845876070
Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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10.60732/43837a12
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DS_40zw467dnc6d_0
Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
Ta2
Ta
A
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[ "Ta" ]
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1
2
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VASP
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null
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null
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MD_2876058403211599652222920
2024-08-16T15:20:31
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MD_9319549970378023349687781
null
[ "bcc_distorted" ]
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2023-12-02T06:25:21
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CO_7750722075837651134128787
Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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[ 3 ]
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[ 1 ]
CC-BY-4.0
{'source-publication': 'https://doi.org/10.1103/PhysRevMaterials.4.093802', 'source-data': 'https://gitlab.com/acclab/gap-data/-/tree/master', 'other': None}
10.60732/43837a12
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DS_40zw467dnc6d_0
Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
Ta6
Ta
A
[ 73, 73, 73, 73, 73, 73 ]
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1
6
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1
VASP
DFT-PBE
null
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null
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null
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MD_2876058403211599652222920
2024-08-16T14:56:48
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PO_6129994183073880286026097
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MD_1175318663105323897157700
null
[ "C15" ]
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2023-12-02T06:25:21
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CO_8550964397925675222606456
Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
[ "Ta" ]
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3,773
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97,332.124136
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2023-12-02T01:26:31
[ [ 1, 1, 1 ] ]
[ 3 ]
2023
[ 1 ]
CC-BY-4.0
{'source-publication': 'https://doi.org/10.1103/PhysRevMaterials.4.093802', 'source-data': 'https://gitlab.com/acclab/gap-data/-/tree/master', 'other': None}
10.60732/43837a12
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DS_40zw467dnc6d_0
Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
Ta121
Ta
A
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MD_2876058403211599652222920
2024-08-16T14:53:50
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2023-12-02T06:25:21
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[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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MD_2876058403211599652222920
2024-08-16T15:35:55
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2023-12-02T06:25:21
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CO_2143798748868609344905116
Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2024-08-16T14:24:11
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2023-12-02T06:25:21
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This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2024-08-16T14:54:01
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2023-12-02T06:25:21
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This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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DS_40zw467dnc6d_0
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2024-08-16T15:26:22
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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DS_40zw467dnc6d_0
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2024-08-16T14:49:52
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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DS_40zw467dnc6d_0
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2024-08-16T14:45:34
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2023-12-02T06:25:21
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[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2024-08-16T14:11:42
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2024-08-16T14:57:17
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2023-12-02T06:25:21
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[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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2024-08-16T15:29:49
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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2024-08-16T15:28:11
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2023-12-02T06:25:21
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[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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DS_40zw467dnc6d_0
Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
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2024-08-16T14:37:05
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2024-08-16T14:27:49
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
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2024-08-16T14:55:27
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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10.60732/43837a12
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DS_40zw467dnc6d_0
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MD_2876058403211599652222920
2024-08-16T15:05:30
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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DS_40zw467dnc6d_0
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2024-08-16T14:50:00
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2024-08-16T14:58:18
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[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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10.60732/43837a12
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2024-08-16T14:18:59
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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10.60732/43837a12
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DS_40zw467dnc6d_0
Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
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2024-08-16T15:19:54
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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10.60732/43837a12
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DS_40zw467dnc6d_0
Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
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Ta
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VASP
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2024-08-16T15:40:59
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2023-12-02T06:25:21
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CO_6625611559703046376028862
Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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10.60732/43837a12
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DS_40zw467dnc6d_0
Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
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Ta
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VASP
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MD_2876058403211599652222920
2024-08-16T14:58:16
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MD_4659839076329245619199735
null
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2023-12-02T06:25:21
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CO_1198794928707588954808390
Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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CC-BY-4.0
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10.60732/43837a12
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DS_40zw467dnc6d_0
Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
Ta12
Ta
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MD_2876058403211599652222920
2024-08-16T14:43:02
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2023-12-02T06:25:21
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CO_1308265563496556023650076
Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
[ "Ta" ]
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2023-12-02T01:26:31
[ [ 1, 1, 1 ] ]
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CC-BY-4.0
{'source-publication': 'https://doi.org/10.1103/PhysRevMaterials.4.093802', 'source-data': 'https://gitlab.com/acclab/gap-data/-/tree/master', 'other': None}
10.60732/43837a12
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DS_40zw467dnc6d_0
Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
Ta6
Ta
A
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MD_2876058403211599652222920
2024-08-16T14:20:19
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MD_1175318663105323897157700
null
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2023-12-02T06:25:21
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CO_1229421731846016494768036
Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
[ [ 1, 1, 1 ] ]
[ 3 ]
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[ 1 ]
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DS_40zw467dnc6d_0
Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
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MD_2876058403211599652222920
2024-08-16T14:17:59
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2023-12-02T06:25:21
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[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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DS_40zw467dnc6d_0
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MD_2876058403211599652222920
2024-08-16T14:49:04
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2023-12-02T06:25:21
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[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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DS_40zw467dnc6d_0
Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
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MD_2876058403211599652222920
2024-08-16T15:17:43
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2023-12-02T06:25:21
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[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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MD_2876058403211599652222920
2024-08-16T15:28:07
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2023-12-02T06:25:21
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CO_8181989736290120471445834
Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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DS_40zw467dnc6d_0
Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
Ta128
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null
[]
null
null
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MD_2876058403211599652222920
2024-08-16T14:20:01
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PO_4986383563386773500964706
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MD_6686363060092726708743168
null
[ "surf_liquid" ]
[ "DS_0shp3qrqk9k9_0", "DS_40zw467dnc6d_0" ]
2023-12-02T06:25:21
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CO_1139230683890405480042013
Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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CC-BY-4.0
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10.60732/43837a12
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DS_40zw467dnc6d_0
Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
Ta2
Ta
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MD_2876058403211599652222920
2024-08-16T15:08:40
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PO_1037009414892544338971075
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MD_9319549970378023349687781
null
[ "bcc_distorted" ]
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2023-12-02T06:25:21
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CO_5846743416206263223775768
Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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CC-BY-4.0
{'source-publication': 'https://doi.org/10.1103/PhysRevMaterials.4.093802', 'source-data': 'https://gitlab.com/acclab/gap-data/-/tree/master', 'other': None}
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DS_40zw467dnc6d_0
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Ta2
Ta
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2024-08-16T15:27:47
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2023-12-02T06:25:21
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[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2024-08-16T14:54:47
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2024-08-16T14:37:26
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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2024-08-16T15:37:58
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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10.60732/43837a12
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DS_40zw467dnc6d_0
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2024-08-16T15:03:50
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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CC-BY-4.0
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DS_40zw467dnc6d_0
Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
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2024-08-16T15:30:33
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2023-12-02T06:25:21
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[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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DS_40zw467dnc6d_0
Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
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Ta
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2024-08-16T15:05:23
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This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2024-08-16T14:18:17
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2023-12-02T06:25:21
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This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2024-08-16T15:21:21
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2023-12-02T06:25:21
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[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2024-08-16T14:20:05
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This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2024-08-16T14:58:10
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2023-12-02T06:25:21
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[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2024-08-16T14:36:01
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2023-12-02T06:25:21
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This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
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2024-08-16T15:04:45
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2023-12-02T06:25:21
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This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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DS_40zw467dnc6d_0
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2024-08-16T14:50:54
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2023-12-02T06:25:21
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This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2024-08-16T15:38:26
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2023-12-02T06:25:21
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This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2024-08-16T14:41:12
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2023-12-02T06:25:21
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[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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DS_40zw467dnc6d_0
Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
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2024-08-16T15:27:26
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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DS_40zw467dnc6d_0
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2024-08-16T14:15:59
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2023-12-02T06:25:21
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[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2024-08-16T15:36:26
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2023-12-02T06:25:21
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This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2024-08-16T14:27:55
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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MD_2876058403211599652222920
2024-08-16T15:05:59
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2023-12-02T06:25:21
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This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2024-08-16T15:30:42
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2024-08-16T14:39:12
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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DS_40zw467dnc6d_0
Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
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2024-08-16T15:11:39
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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DS_40zw467dnc6d_0
Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
Ta12
Ta
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2024-08-16T14:56:15
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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DS_40zw467dnc6d_0
Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
Ta
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2024-08-16T14:39:09
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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DS_40zw467dnc6d_0
Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
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2024-08-16T14:13:20
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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DS_40zw467dnc6d_0
Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
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2024-08-16T14:35:01
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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10.60732/43837a12
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DS_40zw467dnc6d_0
Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
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2024-08-16T14:59:25
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2023-12-02T06:25:21
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[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2024-08-16T15:36:39
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2023-12-02T06:25:21
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[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2024-08-16T14:35:05
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2024-08-16T14:43:17
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2023-12-02T06:25:21
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[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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2024-08-16T15:25:21
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2023-12-02T06:25:21
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[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2024-08-16T14:45:00
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2023-12-02T06:25:21
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[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
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MD_2876058403211599652222920
2024-08-16T14:16:58
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2023-12-02T06:25:21
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[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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MD_2876058403211599652222920
2024-08-16T15:38:32
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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DS_40zw467dnc6d_0
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MD_2876058403211599652222920
2024-08-16T15:04:11
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2023-12-02T06:25:21
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CO_2632959050186416783892431
Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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MD_2876058403211599652222920
2024-08-16T15:20:53
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MD_9785073725370572482662770
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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2024-08-16T14:58:28
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MD_7940789923608474251760237
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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MD_2876058403211599652222920
2024-08-16T14:53:48
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This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2024-08-16T14:42:08
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This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2024-08-16T15:35:32
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[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2024-08-16T15:15:44
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2023-12-02T06:25:21
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This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2024-08-16T15:23:52
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This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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MD_2876058403211599652222920
2024-08-16T14:23:40
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2024-08-16T15:01:44
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2023-12-02T06:25:21
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[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2024-08-16T15:08:43
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2023-12-02T06:25:21
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[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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DS_40zw467dnc6d_0
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MD_2876058403211599652222920
2024-08-16T14:41:31
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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DS_40zw467dnc6d_0
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2024-08-16T14:47:45
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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DS_40zw467dnc6d_0
Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
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2024-08-16T14:47:20
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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DS_40zw467dnc6d_0
Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
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{"POTCAR": "PAW_PBE Ta_pv 07Sep2000", "input": {"EDIFF": "1.00e-06", "ENCUT": "500.000000", "GGA": "PE", "ISMEAR": 1, "KSPACING": "bulk: 0.150000; surface: 1 k-point along surface normal", "LASPH": ".TRUE.", "NELMIN": 4, "PREC": "Accurate", "SIGMA": "0.100000", "file-type": "INCAR"}, "hash": "2876058403211599652222920829484496170262922042889077060703352115774764616890365779643895580266213938769668162332893143343175108070464485555315451520034742", "id": "MD_2876058403211599652222920"}
MD_2876058403211599652222920
2024-08-16T14:20:41
237136518836666868005503928569837000959109125340258127193743185143010687048097661029720836419359066281655500794568686539971683442531517878510081768217245
PO_2371365188366668680055039
{"configuration_type": "bcc_distorted", "hash": "9319549970378023349687781987856218912743726002575544524059222943706411868747729309398918570357811850777050636974140271241076158066208886341758031349401701", "id": "MD_9319549970378023349687781"}
MD_9319549970378023349687781
null
[ "bcc_distorted" ]
[ "DS_40zw467dnc6d_0" ]
2023-12-02T06:25:21
410470166017401777813404197231688369764426691364872512586705885378367803777587544291079707758394098529471070574816150413602962727649406372147699339156984
CO_4104701660174017778134041
Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
[ "Ta" ]
1
3,773
3,775
45,439
0
3,773
0
3,773
0
3,773
-136.87296
97,332.124136
0
2023-12-02T01:26:31
[ [ 1, 1, 1 ] ]
[ 3 ]
2023
[ 1 ]
CC-BY-4.0
{'source-publication': 'https://doi.org/10.1103/PhysRevMaterials.4.093802', 'source-data': 'https://gitlab.com/acclab/gap-data/-/tree/master', 'other': None}
10.60732/43837a12
11519904379436006623820937470671622636399095511006003652433518451041718514865535059337610046166856795128655247286843173994218345029697537757479647159093127
DS_40zw467dnc6d_0
Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
Ta252
Ta
A
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[ "Ta" ]
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1
VASP
DFT-PBE
null
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MD_2876058403211599652222920
2024-08-16T14:24:03
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PO_3555963163406928857643708
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2023-12-02T06:25:21
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CO_3854617591394557818316264
Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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10.60732/43837a12
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DS_40zw467dnc6d_0
Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
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MD_2876058403211599652222920
2024-08-16T14:35:53
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2023-12-02T06:25:21
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CO_1041514078698684381868716
Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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10.60732/43837a12
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DS_40zw467dnc6d_0
Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
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Ta
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VASP
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2024-08-16T14:52:50
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2023-12-02T06:25:21
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[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2024-08-16T14:52:38
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2023-12-02T06:25:21
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[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2024-08-16T15:18:55
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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10.60732/43837a12
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DS_40zw467dnc6d_0
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MD_2876058403211599652222920
2024-08-16T14:30:33
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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2024-08-16T14:14:06
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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2024-08-16T15:24:30
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2023-12-02T06:25:21
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[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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DS_40zw467dnc6d_0
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2024-08-16T15:33:44
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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MD_2876058403211599652222920
2024-08-16T15:41:54
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2023-12-02T06:25:21
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Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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DS_40zw467dnc6d_0
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2024-08-16T15:02:24
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2023-12-02T06:25:21
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CO_1177366218850754565037441
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[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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DS_40zw467dnc6d_0
Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
Ta2
Ta
A
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[ "Ta" ]
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1
2
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VASP
DFT-PBE
null
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MD_2876058403211599652222920
2024-08-16T14:43:36
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PO_4089631534027664669773987
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MD_9319549970378023349687781
null
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2023-12-02T06:25:21
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CO_7840395555896412806486948
Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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CC-BY-4.0
{'source-publication': 'https://doi.org/10.1103/PhysRevMaterials.4.093802', 'source-data': 'https://gitlab.com/acclab/gap-data/-/tree/master', 'other': None}
10.60732/43837a12
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DS_40zw467dnc6d_0
Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
Ta2
Ta
A
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[ "Ta" ]
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1
2
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VASP
DFT-PBE
null
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null
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MD_2876058403211599652222920
2024-08-16T15:12:52
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MD_9319549970378023349687781
null
[ "bcc_distorted" ]
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2023-12-02T06:25:21
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CO_3908938806264956279098994
Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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2023-12-02T01:26:31
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CC-BY-4.0
{'source-publication': 'https://doi.org/10.1103/PhysRevMaterials.4.093802', 'source-data': 'https://gitlab.com/acclab/gap-data/-/tree/master', 'other': None}
10.60732/43837a12
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DS_40zw467dnc6d_0
Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
Ta12
Ta
A
[ 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73, 73 ]
[ "Ta" ]
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1
12
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VASP
DFT-PBE
null
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null
[]
null
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MD_2876058403211599652222920
2024-08-16T14:54:28
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PO_1242492582673097057762201
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MD_4659839076329245619199735
null
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2023-12-02T06:25:21
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CO_6986626872493036775732184
Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
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97,332.124136
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2023-12-02T01:26:31
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CC-BY-4.0
{'source-publication': 'https://doi.org/10.1103/PhysRevMaterials.4.093802', 'source-data': 'https://gitlab.com/acclab/gap-data/-/tree/master', 'other': None}
10.60732/43837a12
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DS_40zw467dnc6d_0
Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0
Ta
Ta
A
[ 73 ]
[ "Ta" ]
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1
1
[ [ 3.2645226526, 0, 0 ], [ 0.194628301298, 3.32265523472, 0 ], [ 1.64501558666, 1.71383366495, 1.64886423016 ] ]
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VASP
DFT-PBE
null
[ [ 0, 0, 0 ] ]
null
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{"POTCAR": "PAW_PBE Ta_pv 07Sep2000", "input": {"EDIFF": "1.00e-06", "ENCUT": "500.000000", "GGA": "PE", "ISMEAR": 1, "KSPACING": "bulk: 0.150000; surface: 1 k-point along surface normal", "LASPH": ".TRUE.", "NELMIN": 4, "PREC": "Accurate", "SIGMA": "0.100000", "file-type": "INCAR"}, "hash": "2876058403211599652222920829484496170262922042889077060703352115774764616890365779643895580266213938769668162332893143343175108070464485555315451520034742", "id": "MD_2876058403211599652222920"}
MD_2876058403211599652222920
2024-08-16T15:17:59
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PO_9506035068552315281162232
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MD_7940789923608474251760237
null
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2023-12-02T06:25:21
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CO_4889935398939856588765618
Ta_PRM2019
[ "Jesper Byggmästar", "Kai Nordlund", "Flyura Djurabekova" ]
This dataset was designed to enable machine-learning of Ta elastic, thermal, and defect properties, as well as surface energetics, melting, and the structure of the liquid phase. The dataset was constructed by starting with the dataset from J. Byggmästar et al., Phys. Rev. B 100, 144105 (2019), then rescaling all of the configurations to the correct lattice spacing and adding in gamma surface configurations.
[ "Ta" ]
1
3,773
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45,439
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97,332.124136
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2023-12-02T01:26:31
[ [ 1, 1, 1 ] ]
[ 3 ]
2023
[ 1 ]
CC-BY-4.0
{'source-publication': 'https://doi.org/10.1103/PhysRevMaterials.4.093802', 'source-data': 'https://gitlab.com/acclab/gap-data/-/tree/master', 'other': None}
10.60732/43837a12
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DS_40zw467dnc6d_0
Ta_PRM2019__Byggmästar-Nordlund-Djurabekova__DS_40zw467dnc6d_0