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chemical_formula_hill
string
chemical_formula_reduced
string
chemical_formula_anonymous
string
atomic_numbers
sequence
elements
sequence
elements_ratios
sequence
nelements
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nsites
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cell
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positions
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pbc
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dimension_types
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int32
structure_hash
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multiplicity
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software
string
method
string
adsorption_energy
float64
atomic_forces
sequence
atomization_energy
float64
cauchy_stress
sequence
cauchy_stress_volume_normalized
bool
electronic_band_gap
float64
electronic_band_gap_type
string
energy
float64
formation_energy
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max_force_norm
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mean_force_norm
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property_object_metadata
string
property_object_metadata_id
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property_object_last_modified
timestamp[ns]
property_object_hash
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property_object_id
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configuration_metadata
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configuration_metadata_id
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configuration_labels
sequence
configuration_names
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configuration_dataset_ids
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configuration_last_modified
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configuration_hash
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configuration_id
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dataset_name
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dataset_authors
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dataset_description
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dataset_elements
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dataset_nelements
int32
dataset_nproperty_objects
int64
dataset_nconfigurations
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dataset_nsites
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dataset_adsorption_energy_count
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dataset_atomic_forces_count
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dataset_atomization_energy_count
int64
dataset_cauchy_stress_count
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dataset_electronic_band_gap_count
int64
dataset_energy_count
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dataset_energy_mean
float64
dataset_energy_variance
float64
dataset_formation_energy_count
int64
dataset_last_modified
timestamp[ns]
dataset_dimension_types
sequence
dataset_nperiodic_dimensions
sequence
dataset_publication_year
string
dataset_total_elements_ratios
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dataset_license
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dataset_links
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dataset_doi
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dataset_hash
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dataset_id
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dataset_extended_id
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C71
C
A
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1
VASP
DFT-PBE
null
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null
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null
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{"input": {"encut": {"value": 520, "units": "eV"}}, "hash": "102439862957345416579435757980411251184715859364031590652706556386856859773466490977891980138017845683216054395454090990042136855050809363628328039055230", "id": "MD_1024398629573454165794357"}
MD_1024398629573454165794357
2024-08-16T14:15:44
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PO_8619791184303698645848539
null
null
null
[ "BR_training__2798", "BB_training__12113", "RR_training__8422", "CA-9_training__15844" ]
[ "DS_fw7m5d8b0fa9_0", "DS_tag5zubl21w8_0", "DS_l7inbtql4ea9_0", "DS_9cdtww9pcjqd_0" ]
2023-12-01T23:56:36
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CO_1000673910794325176040778
CA-9_RR_training
[ "Daniel Hedman", "Tom Rothe", "Gustav Johansson", "Fredrik Sandin", "J. Andreas Larsson", "Yoshiyuki Miyamoto" ]
Random-random configurations from CA-9 dataset used for training NNP_RR potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
[ "C" ]
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2023-12-01T18:56:20
[ [ 1, 1, 1 ] ]
[ 3 ]
2023
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CC-BY-4.0
{'source-publication': 'https://doi.org/10.1016/j.cartre.2021.100027', 'source-data': 'https://doi.org/10.24435/materialscloud:6h-yj', 'other': None}
10.60732/4096ff5c
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DS_tag5zubl21w8_0
CA-9_RR_training__Hedman-Rothe-Johansson-Sandin-Larsson-Miyamoto__DS_tag5zubl21w8_0
C50
C
A
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1
VASP
DFT-PBE
null
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MD_1024398629573454165794357
2024-08-16T14:30:17
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[ "BR_training__9348", "BB_training__6142", "RR_training__19825", "CA-9_training__33526" ]
[ "DS_fw7m5d8b0fa9_0", "DS_tag5zubl21w8_0", "DS_l7inbtql4ea9_0", "DS_9cdtww9pcjqd_0" ]
2023-12-01T23:56:37
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CA-9_RR_training
[ "Daniel Hedman", "Tom Rothe", "Gustav Johansson", "Fredrik Sandin", "J. Andreas Larsson", "Yoshiyuki Miyamoto" ]
Random-random configurations from CA-9 dataset used for training NNP_RR potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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{'source-publication': 'https://doi.org/10.1016/j.cartre.2021.100027', 'source-data': 'https://doi.org/10.24435/materialscloud:6h-yj', 'other': None}
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DS_tag5zubl21w8_0
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VASP
DFT-PBE
null
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MD_1024398629573454165794357
2024-08-16T14:47:57
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null
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[ "RR_training__16539", "CA-9_training__39958" ]
[ "DS_tag5zubl21w8_0", "DS_9cdtww9pcjqd_0" ]
2023-12-01T23:56:39
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CO_1000765941005839685857308
CA-9_RR_training
[ "Daniel Hedman", "Tom Rothe", "Gustav Johansson", "Fredrik Sandin", "J. Andreas Larsson", "Yoshiyuki Miyamoto" ]
Random-random configurations from CA-9 dataset used for training NNP_RR potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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{'source-publication': 'https://doi.org/10.1016/j.cartre.2021.100027', 'source-data': 'https://doi.org/10.24435/materialscloud:6h-yj', 'other': None}
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DS_tag5zubl21w8_0
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MD_1024398629573454165794357
2024-08-16T15:16:47
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[ "BR_training__10570", "BB_training__5020", "RR_training__14530", "CA-9_training__3094" ]
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2023-12-01T23:56:35
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CA-9_RR_training
[ "Daniel Hedman", "Tom Rothe", "Gustav Johansson", "Fredrik Sandin", "J. Andreas Larsson", "Yoshiyuki Miyamoto" ]
Random-random configurations from CA-9 dataset used for training NNP_RR potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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2023-12-01T18:56:20
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{'source-publication': 'https://doi.org/10.1016/j.cartre.2021.100027', 'source-data': 'https://doi.org/10.24435/materialscloud:6h-yj', 'other': None}
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MD_1024398629573454165794357
2024-08-16T14:13:10
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[ "BR_training__7848", "BB_training__9632", "RR_training__8721", "CA-9_validation__5620" ]
[ "DS_fw7m5d8b0fa9_0", "DS_tag5zubl21w8_0", "DS_l7inbtql4ea9_0", "DS_07zjrtacjg3z_0" ]
2023-12-01T23:58:31
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CO_1001542043854732999878024
CA-9_RR_training
[ "Daniel Hedman", "Tom Rothe", "Gustav Johansson", "Fredrik Sandin", "J. Andreas Larsson", "Yoshiyuki Miyamoto" ]
Random-random configurations from CA-9 dataset used for training NNP_RR potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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2023-12-01T18:56:20
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CC-BY-4.0
{'source-publication': 'https://doi.org/10.1016/j.cartre.2021.100027', 'source-data': 'https://doi.org/10.24435/materialscloud:6h-yj', 'other': None}
10.60732/4096ff5c
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DS_tag5zubl21w8_0
CA-9_RR_training__Hedman-Rothe-Johansson-Sandin-Larsson-Miyamoto__DS_tag5zubl21w8_0
C71
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VASP
DFT-PBE
null
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MD_1024398629573454165794357
2024-08-16T15:28:33
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PO_3640172346677568111422812
null
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[ "RR_training__3367", "CA-9_training__16500" ]
[ "DS_tag5zubl21w8_0", "DS_9cdtww9pcjqd_0" ]
2023-12-01T23:56:37
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CO_1002064976878082752136753
CA-9_RR_training
[ "Daniel Hedman", "Tom Rothe", "Gustav Johansson", "Fredrik Sandin", "J. Andreas Larsson", "Yoshiyuki Miyamoto" ]
Random-random configurations from CA-9 dataset used for training NNP_RR potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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2023-12-01T18:56:20
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CC-BY-4.0
{'source-publication': 'https://doi.org/10.1016/j.cartre.2021.100027', 'source-data': 'https://doi.org/10.24435/materialscloud:6h-yj', 'other': None}
10.60732/4096ff5c
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DS_tag5zubl21w8_0
CA-9_RR_training__Hedman-Rothe-Johansson-Sandin-Larsson-Miyamoto__DS_tag5zubl21w8_0
C71
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1
VASP
DFT-PBE
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MD_1024398629573454165794357
2024-08-16T14:58:34
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[ "RR_training__4871", "CA-9_validation__5573" ]
[ "DS_07zjrtacjg3z_0", "DS_tag5zubl21w8_0" ]
2023-12-01T23:58:31
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CA-9_RR_training
[ "Daniel Hedman", "Tom Rothe", "Gustav Johansson", "Fredrik Sandin", "J. Andreas Larsson", "Yoshiyuki Miyamoto" ]
Random-random configurations from CA-9 dataset used for training NNP_RR potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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DS_tag5zubl21w8_0
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DFT-PBE
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MD_1024398629573454165794357
2024-08-16T15:23:20
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2023-12-01T23:56:38
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CA-9_RR_training
[ "Daniel Hedman", "Tom Rothe", "Gustav Johansson", "Fredrik Sandin", "J. Andreas Larsson", "Yoshiyuki Miyamoto" ]
Random-random configurations from CA-9 dataset used for training NNP_RR potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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{'source-publication': 'https://doi.org/10.1016/j.cartre.2021.100027', 'source-data': 'https://doi.org/10.24435/materialscloud:6h-yj', 'other': None}
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DS_tag5zubl21w8_0
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MD_1024398629573454165794357
2024-08-16T15:33:01
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2023-12-01T23:56:35
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CA-9_RR_training
[ "Daniel Hedman", "Tom Rothe", "Gustav Johansson", "Fredrik Sandin", "J. Andreas Larsson", "Yoshiyuki Miyamoto" ]
Random-random configurations from CA-9 dataset used for training NNP_RR potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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MD_1024398629573454165794357
2024-08-16T14:14:51
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2023-12-01T23:56:37
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CA-9_RR_training
[ "Daniel Hedman", "Tom Rothe", "Gustav Johansson", "Fredrik Sandin", "J. Andreas Larsson", "Yoshiyuki Miyamoto" ]
Random-random configurations from CA-9 dataset used for training NNP_RR potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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DS_tag5zubl21w8_0
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MD_1024398629573454165794357
2024-08-16T14:55:50
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[ "BR_training__10276", "BB_training__4903", "RR_training__14954", "CA-9_training__29931" ]
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2023-12-01T23:56:39
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CA-9_RR_training
[ "Daniel Hedman", "Tom Rothe", "Gustav Johansson", "Fredrik Sandin", "J. Andreas Larsson", "Yoshiyuki Miyamoto" ]
Random-random configurations from CA-9 dataset used for training NNP_RR potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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MD_1024398629573454165794357
2024-08-16T14:12:40
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2023-12-01T23:58:31
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CA-9_RR_training
[ "Daniel Hedman", "Tom Rothe", "Gustav Johansson", "Fredrik Sandin", "J. Andreas Larsson", "Yoshiyuki Miyamoto" ]
Random-random configurations from CA-9 dataset used for training NNP_RR potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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{'source-publication': 'https://doi.org/10.1016/j.cartre.2021.100027', 'source-data': 'https://doi.org/10.24435/materialscloud:6h-yj', 'other': None}
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DS_tag5zubl21w8_0
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MD_1024398629573454165794357
2024-08-16T14:48:32
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PO_3655828359598409623381617
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[ "BR_training__575", "BB_training__5428", "RR_training__14973", "CA-9_training__10291" ]
[ "DS_fw7m5d8b0fa9_0", "DS_tag5zubl21w8_0", "DS_l7inbtql4ea9_0", "DS_9cdtww9pcjqd_0" ]
2023-12-01T23:56:35
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CO_1004385844231957397867896
CA-9_RR_training
[ "Daniel Hedman", "Tom Rothe", "Gustav Johansson", "Fredrik Sandin", "J. Andreas Larsson", "Yoshiyuki Miyamoto" ]
Random-random configurations from CA-9 dataset used for training NNP_RR potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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2023-12-01T18:56:20
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{'source-publication': 'https://doi.org/10.1016/j.cartre.2021.100027', 'source-data': 'https://doi.org/10.24435/materialscloud:6h-yj', 'other': None}
10.60732/4096ff5c
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DS_tag5zubl21w8_0
CA-9_RR_training__Hedman-Rothe-Johansson-Sandin-Larsson-Miyamoto__DS_tag5zubl21w8_0
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VASP
DFT-PBE
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{"input": {"encut": {"value": 520, "units": "eV"}}, "hash": "102439862957345416579435757980411251184715859364031590652706556386856859773466490977891980138017845683216054395454090990042136855050809363628328039055230", "id": "MD_1024398629573454165794357"}
MD_1024398629573454165794357
2024-08-16T14:59:39
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[ "BR_training__9825", "BB_training__15434", "RR_training__6234", "CA-9_training__28143" ]
[ "DS_fw7m5d8b0fa9_0", "DS_tag5zubl21w8_0", "DS_l7inbtql4ea9_0", "DS_9cdtww9pcjqd_0" ]
2023-12-01T23:56:37
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CA-9_RR_training
[ "Daniel Hedman", "Tom Rothe", "Gustav Johansson", "Fredrik Sandin", "J. Andreas Larsson", "Yoshiyuki Miyamoto" ]
Random-random configurations from CA-9 dataset used for training NNP_RR potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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MD_1024398629573454165794357
2024-08-16T14:16:34
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2023-12-01T23:56:38
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CA-9_RR_training
[ "Daniel Hedman", "Tom Rothe", "Gustav Johansson", "Fredrik Sandin", "J. Andreas Larsson", "Yoshiyuki Miyamoto" ]
Random-random configurations from CA-9 dataset used for training NNP_RR potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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2023-12-01T18:56:20
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2024-08-16T14:56:57
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2023-12-01T23:56:37
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CA-9_RR_training
[ "Daniel Hedman", "Tom Rothe", "Gustav Johansson", "Fredrik Sandin", "J. Andreas Larsson", "Yoshiyuki Miyamoto" ]
Random-random configurations from CA-9 dataset used for training NNP_RR potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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MD_1024398629573454165794357
2024-08-16T15:01:30
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2023-12-01T23:56:37
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CA-9_RR_training
[ "Daniel Hedman", "Tom Rothe", "Gustav Johansson", "Fredrik Sandin", "J. Andreas Larsson", "Yoshiyuki Miyamoto" ]
Random-random configurations from CA-9 dataset used for training NNP_RR potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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2023-12-01T23:56:38
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CA-9_RR_training
[ "Daniel Hedman", "Tom Rothe", "Gustav Johansson", "Fredrik Sandin", "J. Andreas Larsson", "Yoshiyuki Miyamoto" ]
Random-random configurations from CA-9 dataset used for training NNP_RR potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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MD_1024398629573454165794357
2024-08-16T15:19:12
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[ "BR_validation__1544", "BB_training__9610", "RR_training__10083", "CA-9_training__147" ]
[ "DS_fgakfcwhn9zc_0", "DS_tag5zubl21w8_0", "DS_l7inbtql4ea9_0", "DS_9cdtww9pcjqd_0" ]
2023-12-01T23:56:35
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CO_1005621566529432483553610
CA-9_RR_training
[ "Daniel Hedman", "Tom Rothe", "Gustav Johansson", "Fredrik Sandin", "J. Andreas Larsson", "Yoshiyuki Miyamoto" ]
Random-random configurations from CA-9 dataset used for training NNP_RR potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
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{'source-publication': 'https://doi.org/10.1016/j.cartre.2021.100027', 'source-data': 'https://doi.org/10.24435/materialscloud:6h-yj', 'other': None}
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DFT-PBE
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MD_1024398629573454165794357
2024-08-16T14:49:54
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PO_1291262346786971763425765
null
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null
[ "BR_training__2457", "BB_training__7008", "RR_training__1039", "CA-9_training__39765" ]
[ "DS_fw7m5d8b0fa9_0", "DS_tag5zubl21w8_0", "DS_l7inbtql4ea9_0", "DS_9cdtww9pcjqd_0" ]
2023-12-01T23:56:39
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CO_1005670504621602847288540
CA-9_RR_training
[ "Daniel Hedman", "Tom Rothe", "Gustav Johansson", "Fredrik Sandin", "J. Andreas Larsson", "Yoshiyuki Miyamoto" ]
Random-random configurations from CA-9 dataset used for training NNP_RR potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials
[ "C" ]
1
20,012
20,013
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20,012
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12,796.940875
0
2023-12-01T18:56:20
[ [ 1, 1, 1 ] ]
[ 3 ]
2023
[ 1 ]
CC-BY-4.0
{'source-publication': 'https://doi.org/10.1016/j.cartre.2021.100027', 'source-data': 'https://doi.org/10.24435/materialscloud:6h-yj', 'other': None}
10.60732/4096ff5c
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DS_tag5zubl21w8_0
CA-9_RR_training__Hedman-Rothe-Johansson-Sandin-Larsson-Miyamoto__DS_tag5zubl21w8_0
End of preview. Expand in Data Studio

Dataset

CA-9 RR training

Description

Random-random configurations from CA-9 dataset used for training NNP_RR potential. CA-9 consists of configurations of carbon with curated subsets chosen to test the effects of intentionally choosing dissimilar configurations when training neural network potentials

Additional details stored in dataset columns prepended with "dataset_".

Dataset authors

Daniel Hedman, Tom Rothe, Gustav Johansson, Fredrik Sandin, J. Andreas Larsson, Yoshiyuki Miyamoto

Publication

https://doi.org/10.1016/j.cartre.2021.100027

Original data link

https://doi.org/10.24435/materialscloud:6h-yj

License

CC-BY-4.0

Number of unique molecular configurations

20013

Number of atoms

1100042

Elements included

C

Properties included

energy, atomic forces, cauchy stress

Cite this dataset

Hedman, D., Rothe, T., Johansson, G., Sandin, F., Larsson, J. A., and Miyamoto, Y. CA-9 RR training. ColabFit, 2023. https://doi.org/10.60732/4096ff5c

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