Datasets:
Dataset Viewer
chemical_formula_hill
string | chemical_formula_reduced
string | chemical_formula_anonymous
string | atomic_numbers
sequence | elements
sequence | elements_ratios
sequence | nelements
int32 | nsites
int32 | cell
sequence | positions
sequence | pbc
sequence | dimension_types
sequence | nperiodic_dimensions
int32 | structure_hash
string | multiplicity
int32 | 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
float64 | max_force_norm
float64 | 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
string | configuration_metadata_id
string | configuration_labels
sequence | configuration_names
sequence | configuration_dataset_ids
sequence | configuration_last_modified
timestamp[ns] | configuration_hash
string | configuration_id
string | dataset_name
string | dataset_authors
sequence | dataset_description
string | dataset_elements
sequence | dataset_nelements
int32 | dataset_nproperty_objects
int64 | dataset_nconfigurations
int32 | dataset_nsites
int64 | dataset_adsorption_energy_count
int64 | dataset_atomic_forces_count
int64 | dataset_atomization_energy_count
int64 | dataset_cauchy_stress_count
int64 | dataset_electronic_band_gap_count
int64 | dataset_energy_count
int64 | 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
sequence | dataset_license
string | dataset_links
string | dataset_doi
string | dataset_hash
string | dataset_id
string | dataset_extended_id
string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
H32O16
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H2O
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A2B
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[] | null | null | null | -1,224.841576 | null | 0.023533 | 0.045905 |
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MD_1086845055867124744315202
| 2024-08-16T15:06:44 |
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PO_5573900380092868326210847
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MD_1313719541134552911842234
| null |
[
"input.data.BLYP",
"input.data.BLYP-D3",
"input.data.RPBE",
"input.data.RPBE-D3"
] |
[
"DS_in2572v33lon_0"
] | 2023-12-02T02:45:16 |
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CO_1000007314218519864588175
|
HDNNP_H2O
|
[
"Tobias Morawietz",
"Jörg Behler"
] |
Approximately 28,000 configurations split into 4 datasets, each using a different functional, used in the training of a high-dimensional neural network potential (HDNNP).
|
[
"H",
"O"
] | 2 | 28,675 | 28,678 | 2,327,628 | 0 | 28,675 | 0 | 28,675 | 0 | 28,675 | -2,160.629149 | 3,790,453.171951 | 0 | 2023-12-01T21:52:59 |
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{'source-publication': 'https://doi.org/10.1073/pnas.1602375113', 'source-data': 'https://doi.org/10.5281/zenodo.2634097', 'other': ['https://doi.org/10.1103/PhysRevLett.98.146401']}
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DS_in2572v33lon_0
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HDNNP_H2O__Morawietz-Behler__DS_in2572v33lon_0
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H32O16
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H2O
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[
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CO_1000302267654331255655177
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HDNNP_H2O
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[
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[
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[] | null | null | null | -4,899.805604 | null | 0.053429 | 0.176059 |
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MD_1086845055867124744315202
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PO_5265538289285319906966003
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|
MD_5883389707580648613371193
| null |
[
"input.data.RPBE-D3"
] |
[
"DS_in2572v33lon_0"
] | 2023-12-02T02:45:18 |
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|
CO_1000331607180807100437143
|
HDNNP_H2O
|
[
"Tobias Morawietz",
"Jörg Behler"
] |
Approximately 28,000 configurations split into 4 datasets, each using a different functional, used in the training of a high-dimensional neural network potential (HDNNP).
|
[
"H",
"O"
] | 2 | 28,675 | 28,678 | 2,327,628 | 0 | 28,675 | 0 | 28,675 | 0 | 28,675 | -2,160.629149 | 3,790,453.171951 | 0 | 2023-12-01T21:52:59 |
[
[
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[
3
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2023
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CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1073/pnas.1602375113', 'source-data': 'https://doi.org/10.5281/zenodo.2634097', 'other': ['https://doi.org/10.1103/PhysRevLett.98.146401']}
|
10.60732/4f9f05e5
|
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|
DS_in2572v33lon_0
|
HDNNP_H2O__Morawietz-Behler__DS_in2572v33lon_0
|
H256O128
|
H2O
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A2B
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MD_1086163052438215533988190
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[
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[
"DS_in2572v33lon_0"
] | 2023-12-02T02:45:15 |
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CO_1000538675923983837118439
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HDNNP_H2O
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[
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"Jörg Behler"
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Approximately 28,000 configurations split into 4 datasets, each using a different functional, used in the training of a high-dimensional neural network potential (HDNNP).
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[
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{'source-publication': 'https://doi.org/10.1073/pnas.1602375113', 'source-data': 'https://doi.org/10.5281/zenodo.2634097', 'other': ['https://doi.org/10.1103/PhysRevLett.98.146401']}
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10.60732/4f9f05e5
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DS_in2572v33lon_0
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HDNNP_H2O__Morawietz-Behler__DS_in2572v33lon_0
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H64O32
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H2O
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[
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CO_1000834566609563161993530
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HDNNP_H2O
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[
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Approximately 28,000 configurations split into 4 datasets, each using a different functional, used in the training of a high-dimensional neural network potential (HDNNP).
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[
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"O"
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{'source-publication': 'https://doi.org/10.1073/pnas.1602375113', 'source-data': 'https://doi.org/10.5281/zenodo.2634097', 'other': ['https://doi.org/10.1103/PhysRevLett.98.146401']}
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10.60732/4f9f05e5
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DS_in2572v33lon_0
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H64O32
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[] | null | null | null | -2,450.13461 | null | 0.025877 | 0.063904 |
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MD_1086845055867124744315202
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MD_1153483194126155505397569
| null |
[
"input.data.RPBE-D3",
"input.data.RPBE"
] |
[
"DS_in2572v33lon_0"
] | 2023-12-02T02:45:15 |
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|
CO_1000834566609563161993530
|
HDNNP_H2O
|
[
"Tobias Morawietz",
"Jörg Behler"
] |
Approximately 28,000 configurations split into 4 datasets, each using a different functional, used in the training of a high-dimensional neural network potential (HDNNP).
|
[
"H",
"O"
] | 2 | 28,675 | 28,678 | 2,327,628 | 0 | 28,675 | 0 | 28,675 | 0 | 28,675 | -2,160.629149 | 3,790,453.171951 | 0 | 2023-12-01T21:52:59 |
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3
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2023
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CC-BY-4.0
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{'source-publication': 'https://doi.org/10.1073/pnas.1602375113', 'source-data': 'https://doi.org/10.5281/zenodo.2634097', 'other': ['https://doi.org/10.1103/PhysRevLett.98.146401']}
|
10.60732/4f9f05e5
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|
DS_in2572v33lon_0
|
HDNNP_H2O__Morawietz-Behler__DS_in2572v33lon_0
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H128O64
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H2O
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A2B
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HDNNP_H2O
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[
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{'source-publication': 'https://doi.org/10.1073/pnas.1602375113', 'source-data': 'https://doi.org/10.5281/zenodo.2634097', 'other': ['https://doi.org/10.1103/PhysRevLett.98.146401']}
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10.60732/4f9f05e5
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DS_in2572v33lon_0
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HDNNP_H2O__Morawietz-Behler__DS_in2572v33lon_0
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H32O16
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H2O
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A2B
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FHI-aims
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[] | null | null | null | -1,224.824423 | null | 0.04069 | 0.11402 |
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|
MD_1086845055867124744315202
| 2024-08-16T14:51:38 |
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PO_1166846292051008911857961
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MD_8054451078514177065173282
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[
"input.data.BLYP-D3",
"input.data.RPBE-D3",
"input.data.RPBE",
"input.data.BLYP"
] |
[
"DS_in2572v33lon_0"
] | 2023-12-02T02:45:17 |
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|
CO_1001151501808743851260095
|
HDNNP_H2O
|
[
"Tobias Morawietz",
"Jörg Behler"
] |
Approximately 28,000 configurations split into 4 datasets, each using a different functional, used in the training of a high-dimensional neural network potential (HDNNP).
|
[
"H",
"O"
] | 2 | 28,675 | 28,678 | 2,327,628 | 0 | 28,675 | 0 | 28,675 | 0 | 28,675 | -2,160.629149 | 3,790,453.171951 | 0 | 2023-12-01T21:52:59 |
[
[
1,
1,
1
]
] |
[
3
] |
2023
|
[
0.6666666666666666,
0.3333333333333333
] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1073/pnas.1602375113', 'source-data': 'https://doi.org/10.5281/zenodo.2634097', 'other': ['https://doi.org/10.1103/PhysRevLett.98.146401']}
|
10.60732/4f9f05e5
|
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|
DS_in2572v33lon_0
|
HDNNP_H2O__Morawietz-Behler__DS_in2572v33lon_0
|
End of preview. Expand
in Data Studio
Dataset
HDNNP H2O
Description
Approximately 28,000 configurations split into 4 datasets, each using a different functional, used in the training of a high-dimensional neural network potential (HDNNP).
Additional details stored in dataset columns prepended with "dataset_".
Dataset authors
Tobias Morawietz, Jörg Behler
Publication
https://doi.org/10.1073/pnas.1602375113
Original data link
https://doi.org/10.5281/zenodo.2634097
License
CC-BY-4.0
Number of unique molecular configurations
28678
Number of atoms
2327628
Elements included
H, O
Properties included
energy, atomic forces, cauchy stress
Cite this dataset
Morawietz, T., and Behler, J. HDNNP H2O. ColabFit, 2023. https://doi.org/10.60732/4f9f05e5
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