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 |
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Si64
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Si
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A
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DFT-PBE
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{"input": {"AVE_TEMP(K)": 1298.4, "DESIRED_TEMP(K)": 1300.0, "METHOD(1-VV,2-NH,3-LV,4-LVPR,5-NHRP)": 2, "TEMP(K)": 1580.8, "TIME(fs)": 442.0, "TIME_INTERVAL(fs)": 200.0, "TOT_TEMP(K)": 287100.0}, "property_keys": {"energy": "eTot", "forces": "Force", "stress": "Pressure Internal"}, "hash": "5035703956848864287936013061244247686098497353989614177502192599985792254418183456045010183242427091846653468670417021192150539908784262312021023349728624", "id": "MD_5035703956848864287936013"}
|
MD_5035703956848864287936013
| 2024-10-15T15:49:02 |
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|
PO_8885918381420378371349324
| null | null |
[
"temperature:1300",
"frame:220"
] |
[
"Si_4600images__1300k__MOVEMENT__220"
] |
[
"DS_cgjdk1e2txjy_0"
] | 2024-10-15T19:56:30 |
1000879849038922546
|
CO_1000879849038922546
|
PWMLFF_feature_comparison_NPJ2023
|
[
"Ting Han",
"Jie Li",
"Liping Liu",
"Fengyu Li",
"Lin-Wang Wang"
] |
Partial dataset for "Accuracy evaluation of different machine learning force field features". The included data is limited to that hosted directly on the repository at the related GitHub link. From publication abstract: Predicting energies and forces using machine learning force field (MLFF) depends on accurate descriptions (features) of chemical environment. Despite the numerous features proposed, there is a lack of controlled comparison among them for their universality and accuracy. In this work, we compared several commonly used feature types for their ability to describe physical systems. These different feature types include cosine feature, Gaussian feature, moment tensor potential (MTP) feature, spectral neighbor analysis potential feature, simplified smooth deep potential with Chebyshev polynomials feature and Gaussian polynomials feature, and atomic cluster expansion feature. We evaluated the training root mean square error (RMSE) for the atomic group energy, total energy, and force using linear regression model regarding to the density functional theory results. We applied these MLFF models to an amorphous sulfur system and carbon systems, and the fitting results show that MTP feature can yield the smallest RMSE results compared with other feature types for either sulfur system or carbon system in the disordered atomic configurations. Moreover, as an extending test of other systems, the MTP feature combined with linear regression model can also reproduce similar quantities along the ab initio molecular dynamics trajectory as represented by Cu systems. Our results are helpful in selecting the proper features for the MLFF development.
|
[
"C",
"H",
"Mg",
"Ni",
"O",
"Si"
] | 6 | 17,255 | 17,255 | 918,240 | 0 | 17,255 | 0 | 17,255 | 0 | 17,255 | -94,982.575812 | 28,080,747,868.058937 | 0 | 2024-10-15T15:57:14 |
[
[
1,
1,
1
]
] |
[
3
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2024
|
[
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] |
CC-BY-4.0
|
{'source-publication': 'https://www.doi.org/10.1088/1367-2630/acf2bb', 'source-data': 'https://github.com/LonxunQuantum/PWMLFF_library/tree/main', 'other': None}
| null |
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|
DS_cgjdk1e2txjy_0
|
PWMLFF_feature_comparison_NPJ2023__Han-Li-Liu-Li-Wang__DS_cgjdk1e2txjy_0
|
Si64
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Si
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MD_3747320956759582302895978
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[
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1000910203604432536
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CO_1000910203604432536
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PWMLFF_feature_comparison_NPJ2023
|
[
"Ting Han",
"Jie Li",
"Liping Liu",
"Fengyu Li",
"Lin-Wang Wang"
] |
Partial dataset for "Accuracy evaluation of different machine learning force field features". The included data is limited to that hosted directly on the repository at the related GitHub link. From publication abstract: Predicting energies and forces using machine learning force field (MLFF) depends on accurate descriptions (features) of chemical environment. Despite the numerous features proposed, there is a lack of controlled comparison among them for their universality and accuracy. In this work, we compared several commonly used feature types for their ability to describe physical systems. These different feature types include cosine feature, Gaussian feature, moment tensor potential (MTP) feature, spectral neighbor analysis potential feature, simplified smooth deep potential with Chebyshev polynomials feature and Gaussian polynomials feature, and atomic cluster expansion feature. We evaluated the training root mean square error (RMSE) for the atomic group energy, total energy, and force using linear regression model regarding to the density functional theory results. We applied these MLFF models to an amorphous sulfur system and carbon systems, and the fitting results show that MTP feature can yield the smallest RMSE results compared with other feature types for either sulfur system or carbon system in the disordered atomic configurations. Moreover, as an extending test of other systems, the MTP feature combined with linear regression model can also reproduce similar quantities along the ab initio molecular dynamics trajectory as represented by Cu systems. Our results are helpful in selecting the proper features for the MLFF development.
|
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{'source-publication': 'https://www.doi.org/10.1088/1367-2630/acf2bb', 'source-data': 'https://github.com/LonxunQuantum/PWMLFF_library/tree/main', 'other': None}
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MD_4082455644921501412015841
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[
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1001125620532144375
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CO_1001125620532144375
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PWMLFF_feature_comparison_NPJ2023
|
[
"Ting Han",
"Jie Li",
"Liping Liu",
"Fengyu Li",
"Lin-Wang Wang"
] |
Partial dataset for "Accuracy evaluation of different machine learning force field features". The included data is limited to that hosted directly on the repository at the related GitHub link. From publication abstract: Predicting energies and forces using machine learning force field (MLFF) depends on accurate descriptions (features) of chemical environment. Despite the numerous features proposed, there is a lack of controlled comparison among them for their universality and accuracy. In this work, we compared several commonly used feature types for their ability to describe physical systems. These different feature types include cosine feature, Gaussian feature, moment tensor potential (MTP) feature, spectral neighbor analysis potential feature, simplified smooth deep potential with Chebyshev polynomials feature and Gaussian polynomials feature, and atomic cluster expansion feature. We evaluated the training root mean square error (RMSE) for the atomic group energy, total energy, and force using linear regression model regarding to the density functional theory results. We applied these MLFF models to an amorphous sulfur system and carbon systems, and the fitting results show that MTP feature can yield the smallest RMSE results compared with other feature types for either sulfur system or carbon system in the disordered atomic configurations. Moreover, as an extending test of other systems, the MTP feature combined with linear regression model can also reproduce similar quantities along the ab initio molecular dynamics trajectory as represented by Cu systems. Our results are helpful in selecting the proper features for the MLFF development.
|
[
"C",
"H",
"Mg",
"Ni",
"O",
"Si"
] | 6 | 17,255 | 17,255 | 918,240 | 0 | 17,255 | 0 | 17,255 | 0 | 17,255 | -94,982.575812 | 28,080,747,868.058937 | 0 | 2024-10-15T15:57:14 |
[
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1,
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[
3
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2024
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0.15290120230005227,
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CC-BY-4.0
|
{'source-publication': 'https://www.doi.org/10.1088/1367-2630/acf2bb', 'source-data': 'https://github.com/LonxunQuantum/PWMLFF_library/tree/main', 'other': None}
| null |
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DS_cgjdk1e2txjy_0
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PWMLFF_feature_comparison_NPJ2023__Han-Li-Liu-Li-Wang__DS_cgjdk1e2txjy_0
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Ni108
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Ni
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A
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MD_1820078414368772547204569
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CO_1001385718573989161
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PWMLFF_feature_comparison_NPJ2023
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[
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Partial dataset for "Accuracy evaluation of different machine learning force field features". The included data is limited to that hosted directly on the repository at the related GitHub link. From publication abstract: Predicting energies and forces using machine learning force field (MLFF) depends on accurate descriptions (features) of chemical environment. Despite the numerous features proposed, there is a lack of controlled comparison among them for their universality and accuracy. In this work, we compared several commonly used feature types for their ability to describe physical systems. These different feature types include cosine feature, Gaussian feature, moment tensor potential (MTP) feature, spectral neighbor analysis potential feature, simplified smooth deep potential with Chebyshev polynomials feature and Gaussian polynomials feature, and atomic cluster expansion feature. We evaluated the training root mean square error (RMSE) for the atomic group energy, total energy, and force using linear regression model regarding to the density functional theory results. We applied these MLFF models to an amorphous sulfur system and carbon systems, and the fitting results show that MTP feature can yield the smallest RMSE results compared with other feature types for either sulfur system or carbon system in the disordered atomic configurations. Moreover, as an extending test of other systems, the MTP feature combined with linear regression model can also reproduce similar quantities along the ab initio molecular dynamics trajectory as represented by Cu systems. Our results are helpful in selecting the proper features for the MLFF development.
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MD_5198098237997834016601898
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[
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1001436025596295197
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CO_1001436025596295197
|
PWMLFF_feature_comparison_NPJ2023
|
[
"Ting Han",
"Jie Li",
"Liping Liu",
"Fengyu Li",
"Lin-Wang Wang"
] |
Partial dataset for "Accuracy evaluation of different machine learning force field features". The included data is limited to that hosted directly on the repository at the related GitHub link. From publication abstract: Predicting energies and forces using machine learning force field (MLFF) depends on accurate descriptions (features) of chemical environment. Despite the numerous features proposed, there is a lack of controlled comparison among them for their universality and accuracy. In this work, we compared several commonly used feature types for their ability to describe physical systems. These different feature types include cosine feature, Gaussian feature, moment tensor potential (MTP) feature, spectral neighbor analysis potential feature, simplified smooth deep potential with Chebyshev polynomials feature and Gaussian polynomials feature, and atomic cluster expansion feature. We evaluated the training root mean square error (RMSE) for the atomic group energy, total energy, and force using linear regression model regarding to the density functional theory results. We applied these MLFF models to an amorphous sulfur system and carbon systems, and the fitting results show that MTP feature can yield the smallest RMSE results compared with other feature types for either sulfur system or carbon system in the disordered atomic configurations. Moreover, as an extending test of other systems, the MTP feature combined with linear regression model can also reproduce similar quantities along the ab initio molecular dynamics trajectory as represented by Cu systems. Our results are helpful in selecting the proper features for the MLFF development.
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[
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[
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[
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1001895022053885287
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CO_1001895022053885287
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PWMLFF_feature_comparison_NPJ2023
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[
"Ting Han",
"Jie Li",
"Liping Liu",
"Fengyu Li",
"Lin-Wang Wang"
] |
Partial dataset for "Accuracy evaluation of different machine learning force field features". The included data is limited to that hosted directly on the repository at the related GitHub link. From publication abstract: Predicting energies and forces using machine learning force field (MLFF) depends on accurate descriptions (features) of chemical environment. Despite the numerous features proposed, there is a lack of controlled comparison among them for their universality and accuracy. In this work, we compared several commonly used feature types for their ability to describe physical systems. These different feature types include cosine feature, Gaussian feature, moment tensor potential (MTP) feature, spectral neighbor analysis potential feature, simplified smooth deep potential with Chebyshev polynomials feature and Gaussian polynomials feature, and atomic cluster expansion feature. We evaluated the training root mean square error (RMSE) for the atomic group energy, total energy, and force using linear regression model regarding to the density functional theory results. We applied these MLFF models to an amorphous sulfur system and carbon systems, and the fitting results show that MTP feature can yield the smallest RMSE results compared with other feature types for either sulfur system or carbon system in the disordered atomic configurations. Moreover, as an extending test of other systems, the MTP feature combined with linear regression model can also reproduce similar quantities along the ab initio molecular dynamics trajectory as represented by Cu systems. Our results are helpful in selecting the proper features for the MLFF development.
|
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[
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[
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1001941704917984481
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CO_1001941704917984481
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PWMLFF_feature_comparison_NPJ2023
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[
"Ting Han",
"Jie Li",
"Liping Liu",
"Fengyu Li",
"Lin-Wang Wang"
] |
Partial dataset for "Accuracy evaluation of different machine learning force field features". The included data is limited to that hosted directly on the repository at the related GitHub link. From publication abstract: Predicting energies and forces using machine learning force field (MLFF) depends on accurate descriptions (features) of chemical environment. Despite the numerous features proposed, there is a lack of controlled comparison among them for their universality and accuracy. In this work, we compared several commonly used feature types for their ability to describe physical systems. These different feature types include cosine feature, Gaussian feature, moment tensor potential (MTP) feature, spectral neighbor analysis potential feature, simplified smooth deep potential with Chebyshev polynomials feature and Gaussian polynomials feature, and atomic cluster expansion feature. We evaluated the training root mean square error (RMSE) for the atomic group energy, total energy, and force using linear regression model regarding to the density functional theory results. We applied these MLFF models to an amorphous sulfur system and carbon systems, and the fitting results show that MTP feature can yield the smallest RMSE results compared with other feature types for either sulfur system or carbon system in the disordered atomic configurations. Moreover, as an extending test of other systems, the MTP feature combined with linear regression model can also reproduce similar quantities along the ab initio molecular dynamics trajectory as represented by Cu systems. Our results are helpful in selecting the proper features for the MLFF development.
|
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{'source-publication': 'https://www.doi.org/10.1088/1367-2630/acf2bb', 'source-data': 'https://github.com/LonxunQuantum/PWMLFF_library/tree/main', 'other': None}
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[
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[
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1001943598167242547
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CO_1001943598167242547
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PWMLFF_feature_comparison_NPJ2023
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[
"Ting Han",
"Jie Li",
"Liping Liu",
"Fengyu Li",
"Lin-Wang Wang"
] |
Partial dataset for "Accuracy evaluation of different machine learning force field features". The included data is limited to that hosted directly on the repository at the related GitHub link. From publication abstract: Predicting energies and forces using machine learning force field (MLFF) depends on accurate descriptions (features) of chemical environment. Despite the numerous features proposed, there is a lack of controlled comparison among them for their universality and accuracy. In this work, we compared several commonly used feature types for their ability to describe physical systems. These different feature types include cosine feature, Gaussian feature, moment tensor potential (MTP) feature, spectral neighbor analysis potential feature, simplified smooth deep potential with Chebyshev polynomials feature and Gaussian polynomials feature, and atomic cluster expansion feature. We evaluated the training root mean square error (RMSE) for the atomic group energy, total energy, and force using linear regression model regarding to the density functional theory results. We applied these MLFF models to an amorphous sulfur system and carbon systems, and the fitting results show that MTP feature can yield the smallest RMSE results compared with other feature types for either sulfur system or carbon system in the disordered atomic configurations. Moreover, as an extending test of other systems, the MTP feature combined with linear regression model can also reproduce similar quantities along the ab initio molecular dynamics trajectory as represented by Cu systems. Our results are helpful in selecting the proper features for the MLFF development.
|
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{'source-publication': 'https://www.doi.org/10.1088/1367-2630/acf2bb', 'source-data': 'https://github.com/LonxunQuantum/PWMLFF_library/tree/main', 'other': None}
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Partial dataset for "Accuracy evaluation of different machine learning force field features". The included data is limited to that hosted directly on the repository at the related GitHub link. From publication abstract: Predicting energies and forces using machine learning force field (MLFF) depends on accurate descriptions (features) of chemical environment. Despite the numerous features proposed, there is a lack of controlled comparison among them for their universality and accuracy. In this work, we compared several commonly used feature types for their ability to describe physical systems. These different feature types include cosine feature, Gaussian feature, moment tensor potential (MTP) feature, spectral neighbor analysis potential feature, simplified smooth deep potential with Chebyshev polynomials feature and Gaussian polynomials feature, and atomic cluster expansion feature. We evaluated the training root mean square error (RMSE) for the atomic group energy, total energy, and force using linear regression model regarding to the density functional theory results. We applied these MLFF models to an amorphous sulfur system and carbon systems, and the fitting results show that MTP feature can yield the smallest RMSE results compared with other feature types for either sulfur system or carbon system in the disordered atomic configurations. Moreover, as an extending test of other systems, the MTP feature combined with linear regression model can also reproduce similar quantities along the ab initio molecular dynamics trajectory as represented by Cu systems. Our results are helpful in selecting the proper features for the MLFF development.
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CO_1002675158960473157
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PWMLFF_feature_comparison_NPJ2023
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[
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Partial dataset for "Accuracy evaluation of different machine learning force field features". The included data is limited to that hosted directly on the repository at the related GitHub link. From publication abstract: Predicting energies and forces using machine learning force field (MLFF) depends on accurate descriptions (features) of chemical environment. Despite the numerous features proposed, there is a lack of controlled comparison among them for their universality and accuracy. In this work, we compared several commonly used feature types for their ability to describe physical systems. These different feature types include cosine feature, Gaussian feature, moment tensor potential (MTP) feature, spectral neighbor analysis potential feature, simplified smooth deep potential with Chebyshev polynomials feature and Gaussian polynomials feature, and atomic cluster expansion feature. We evaluated the training root mean square error (RMSE) for the atomic group energy, total energy, and force using linear regression model regarding to the density functional theory results. We applied these MLFF models to an amorphous sulfur system and carbon systems, and the fitting results show that MTP feature can yield the smallest RMSE results compared with other feature types for either sulfur system or carbon system in the disordered atomic configurations. Moreover, as an extending test of other systems, the MTP feature combined with linear regression model can also reproduce similar quantities along the ab initio molecular dynamics trajectory as represented by Cu systems. Our results are helpful in selecting the proper features for the MLFF development.
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Partial dataset for "Accuracy evaluation of different machine learning force field features". The included data is limited to that hosted directly on the repository at the related GitHub link. From publication abstract: Predicting energies and forces using machine learning force field (MLFF) depends on accurate descriptions (features) of chemical environment. Despite the numerous features proposed, there is a lack of controlled comparison among them for their universality and accuracy. In this work, we compared several commonly used feature types for their ability to describe physical systems. These different feature types include cosine feature, Gaussian feature, moment tensor potential (MTP) feature, spectral neighbor analysis potential feature, simplified smooth deep potential with Chebyshev polynomials feature and Gaussian polynomials feature, and atomic cluster expansion feature. We evaluated the training root mean square error (RMSE) for the atomic group energy, total energy, and force using linear regression model regarding to the density functional theory results. We applied these MLFF models to an amorphous sulfur system and carbon systems, and the fitting results show that MTP feature can yield the smallest RMSE results compared with other feature types for either sulfur system or carbon system in the disordered atomic configurations. Moreover, as an extending test of other systems, the MTP feature combined with linear regression model can also reproduce similar quantities along the ab initio molecular dynamics trajectory as represented by Cu systems. Our results are helpful in selecting the proper features for the MLFF development.
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CO_1003247687338028165
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PWMLFF_feature_comparison_NPJ2023
|
[
"Ting Han",
"Jie Li",
"Liping Liu",
"Fengyu Li",
"Lin-Wang Wang"
] |
Partial dataset for "Accuracy evaluation of different machine learning force field features". The included data is limited to that hosted directly on the repository at the related GitHub link. From publication abstract: Predicting energies and forces using machine learning force field (MLFF) depends on accurate descriptions (features) of chemical environment. Despite the numerous features proposed, there is a lack of controlled comparison among them for their universality and accuracy. In this work, we compared several commonly used feature types for their ability to describe physical systems. These different feature types include cosine feature, Gaussian feature, moment tensor potential (MTP) feature, spectral neighbor analysis potential feature, simplified smooth deep potential with Chebyshev polynomials feature and Gaussian polynomials feature, and atomic cluster expansion feature. We evaluated the training root mean square error (RMSE) for the atomic group energy, total energy, and force using linear regression model regarding to the density functional theory results. We applied these MLFF models to an amorphous sulfur system and carbon systems, and the fitting results show that MTP feature can yield the smallest RMSE results compared with other feature types for either sulfur system or carbon system in the disordered atomic configurations. Moreover, as an extending test of other systems, the MTP feature combined with linear regression model can also reproduce similar quantities along the ab initio molecular dynamics trajectory as represented by Cu systems. Our results are helpful in selecting the proper features for the MLFF development.
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MD_8366467386804259668706974
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PO_4275754202974726172534611
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[
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[
"Ni__800k__MOVEMENT__325"
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[
"DS_cgjdk1e2txjy_0"
] | 2024-10-15T19:55:37 |
1003257958909657767
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CO_1003257958909657767
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PWMLFF_feature_comparison_NPJ2023
|
[
"Ting Han",
"Jie Li",
"Liping Liu",
"Fengyu Li",
"Lin-Wang Wang"
] |
Partial dataset for "Accuracy evaluation of different machine learning force field features". The included data is limited to that hosted directly on the repository at the related GitHub link. From publication abstract: Predicting energies and forces using machine learning force field (MLFF) depends on accurate descriptions (features) of chemical environment. Despite the numerous features proposed, there is a lack of controlled comparison among them for their universality and accuracy. In this work, we compared several commonly used feature types for their ability to describe physical systems. These different feature types include cosine feature, Gaussian feature, moment tensor potential (MTP) feature, spectral neighbor analysis potential feature, simplified smooth deep potential with Chebyshev polynomials feature and Gaussian polynomials feature, and atomic cluster expansion feature. We evaluated the training root mean square error (RMSE) for the atomic group energy, total energy, and force using linear regression model regarding to the density functional theory results. We applied these MLFF models to an amorphous sulfur system and carbon systems, and the fitting results show that MTP feature can yield the smallest RMSE results compared with other feature types for either sulfur system or carbon system in the disordered atomic configurations. Moreover, as an extending test of other systems, the MTP feature combined with linear regression model can also reproduce similar quantities along the ab initio molecular dynamics trajectory as represented by Cu systems. Our results are helpful in selecting the proper features for the MLFF development.
|
[
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"Ni",
"O",
"Si"
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{'source-publication': 'https://www.doi.org/10.1088/1367-2630/acf2bb', 'source-data': 'https://github.com/LonxunQuantum/PWMLFF_library/tree/main', 'other': None}
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DS_cgjdk1e2txjy_0
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PO_1202620475668739837326795
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[
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[
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[
"DS_cgjdk1e2txjy_0"
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1003313337101191427
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CO_1003313337101191427
|
PWMLFF_feature_comparison_NPJ2023
|
[
"Ting Han",
"Jie Li",
"Liping Liu",
"Fengyu Li",
"Lin-Wang Wang"
] |
Partial dataset for "Accuracy evaluation of different machine learning force field features". The included data is limited to that hosted directly on the repository at the related GitHub link. From publication abstract: Predicting energies and forces using machine learning force field (MLFF) depends on accurate descriptions (features) of chemical environment. Despite the numerous features proposed, there is a lack of controlled comparison among them for their universality and accuracy. In this work, we compared several commonly used feature types for their ability to describe physical systems. These different feature types include cosine feature, Gaussian feature, moment tensor potential (MTP) feature, spectral neighbor analysis potential feature, simplified smooth deep potential with Chebyshev polynomials feature and Gaussian polynomials feature, and atomic cluster expansion feature. We evaluated the training root mean square error (RMSE) for the atomic group energy, total energy, and force using linear regression model regarding to the density functional theory results. We applied these MLFF models to an amorphous sulfur system and carbon systems, and the fitting results show that MTP feature can yield the smallest RMSE results compared with other feature types for either sulfur system or carbon system in the disordered atomic configurations. Moreover, as an extending test of other systems, the MTP feature combined with linear regression model can also reproduce similar quantities along the ab initio molecular dynamics trajectory as represented by Cu systems. Our results are helpful in selecting the proper features for the MLFF development.
|
[
"C",
"H",
"Mg",
"Ni",
"O",
"Si"
] | 6 | 17,255 | 17,255 | 918,240 | 0 | 17,255 | 0 | 17,255 | 0 | 17,255 | -94,982.575812 | 28,080,747,868.058937 | 0 | 2024-10-15T15:57:14 |
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| null |
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DS_cgjdk1e2txjy_0
|
PWMLFF_feature_comparison_NPJ2023__Han-Li-Liu-Li-Wang__DS_cgjdk1e2txjy_0
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CH4
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CH4
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A4B
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[
true,
true,
true
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[
1,
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1
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| 1 |
PWmat
|
DFT-PBE
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|
MD_1139460882062839638136517
| 2024-10-15T15:45:17 |
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|
PO_4174556625210850789298731
| null | null |
[
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[
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[
"DS_cgjdk1e2txjy_0"
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1004462224264435560
|
CO_1004462224264435560
|
PWMLFF_feature_comparison_NPJ2023
|
[
"Ting Han",
"Jie Li",
"Liping Liu",
"Fengyu Li",
"Lin-Wang Wang"
] |
Partial dataset for "Accuracy evaluation of different machine learning force field features". The included data is limited to that hosted directly on the repository at the related GitHub link. From publication abstract: Predicting energies and forces using machine learning force field (MLFF) depends on accurate descriptions (features) of chemical environment. Despite the numerous features proposed, there is a lack of controlled comparison among them for their universality and accuracy. In this work, we compared several commonly used feature types for their ability to describe physical systems. These different feature types include cosine feature, Gaussian feature, moment tensor potential (MTP) feature, spectral neighbor analysis potential feature, simplified smooth deep potential with Chebyshev polynomials feature and Gaussian polynomials feature, and atomic cluster expansion feature. We evaluated the training root mean square error (RMSE) for the atomic group energy, total energy, and force using linear regression model regarding to the density functional theory results. We applied these MLFF models to an amorphous sulfur system and carbon systems, and the fitting results show that MTP feature can yield the smallest RMSE results compared with other feature types for either sulfur system or carbon system in the disordered atomic configurations. Moreover, as an extending test of other systems, the MTP feature combined with linear regression model can also reproduce similar quantities along the ab initio molecular dynamics trajectory as represented by Cu systems. Our results are helpful in selecting the proper features for the MLFF development.
|
[
"C",
"H",
"Mg",
"Ni",
"O",
"Si"
] | 6 | 17,255 | 17,255 | 918,240 | 0 | 17,255 | 0 | 17,255 | 0 | 17,255 | -94,982.575812 | 28,080,747,868.058937 | 0 | 2024-10-15T15:57:14 |
[
[
1,
1,
1
]
] |
[
3
] |
2024
|
[
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0.15290120230005227,
0.35931782540512286,
0.001633559853633037,
0.3206133472730441
] |
CC-BY-4.0
|
{'source-publication': 'https://www.doi.org/10.1088/1367-2630/acf2bb', 'source-data': 'https://github.com/LonxunQuantum/PWMLFF_library/tree/main', 'other': None}
| null |
3416353886768184069498309577448540463118046042128164727481437935635525348649148828592205659488310261220439788024414920747258168148334049671414505074802036
|
DS_cgjdk1e2txjy_0
|
PWMLFF_feature_comparison_NPJ2023__Han-Li-Liu-Li-Wang__DS_cgjdk1e2txjy_0
|
End of preview. Expand
in Data Studio
Dataset
PWMLFF feature comparison NPJ2023
Description
Partial dataset for "Accuracy evaluation of different machine learning force field features". The included data is limited to that hosted directly on the repository at the related GitHub link. From publication abstract: Predicting energies and forces using machine learning force field (MLFF) depends on accurate descriptions (features) of chemical environment. Despite the numerous features proposed, there is a lack of controlled comparison among them for their universality and accuracy. In this work, we compared several commonly used feature types for their ability to describe physical systems. These different feature types include cosine feature, Gaussian feature, moment tensor potential (MTP) feature, spectral neighbor analysis potential feature, simplified smooth deep potential with Chebyshev polynomials feature and Gaussian polynomials feature, and atomic cluster expansion feature. We evaluated the training root mean square error (RMSE) for the atomic group energy, total energy, and force using linear regression model regarding to the density functional theory results. We applied these MLFF models to an amorphous sulfur system and carbon systems, and the fitting results show that MTP feature can yield the smallest RMSE results compared with other feature types for either sulfur system or carbon system in the disordered atomic configurations. Moreover, as an extending test of other systems, the MTP feature combined with linear regression model can also reproduce similar quantities along the ab initio molecular dynamics trajectory as represented by Cu systems. Our results are helpful in selecting the proper features for the MLFF development.
Additional details stored in dataset columns prepended with "dataset_".
Dataset authors
Ting Han, Jie Li, Liping Liu, Fengyu Li, Lin-Wang Wang
Publication
https://www.doi.org/10.1088/1367-2630/acf2bb
Original data link
https://github.com/LonxunQuantum/PWMLFF_library/tree/main
License
CC-BY-4.0
Number of unique molecular configurations
17255
Number of atoms
918240
Elements included
C, H, Mg, Ni, O, Si
Properties included
energy, atomic forces, cauchy stress
Cite this dataset
Han, T., Li, J., Liu, L., Li, F., and Wang, L. PWMLFF feature comparison NPJ2023. ColabFit, 2024. https://doi.org/10.60732/209e0c9c
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