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
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string | configuration_id
string | dataset_name
string | dataset_authors
sequence | dataset_description
string | dataset_elements
sequence | dataset_nelements
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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
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sequence | dataset_nperiodic_dimensions
sequence | dataset_publication_year
string | dataset_total_elements_ratios
sequence | dataset_license
string | dataset_links
string | dataset_doi
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string | dataset_id
string | dataset_extended_id
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Hf32O64
|
HfO2
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A2B
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| 1 |
VASP
|
DFT-PBE
| null |
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] | null |
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] | true | null | null | -935.664956 | null | 2.223078 | 10.240678 |
{"input": {"ediff": {"value": 0.002, "units": "eV"}, "ediffg": {"value": 0.05, "units": "eV/angstrom"}}, "hash": "3220989785209901592711512060749399143158255874818296585998205381974360544737732089417139158924615074837033519915139922028460460046863434186179457886861933", "id": "MD_3220989785209901592711512"}
|
MD_3220989785209901592711512
| 2024-08-16T14:51:40 |
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|
PO_1195257746382411456665436
| null | null | null |
[
"sait_semiconductors__HfO__Trainset_15976",
"sait_semiconductors__HfO_raw__Random__6_10008"
] |
[
"DS_ekrypue10aay_0",
"DS_ppk44zeithjj_0"
] | 2024-01-10T14:15:08 |
10003036070598618287976735933585432525844859106889656079934749963696999426992600558493823196196041445985467827830742507345963165352185823653757323673022322
|
CO_1000303607059861828797673
|
SAIT_semiconductors_ACS_2023_HfO_train
|
[
"Geonu Kim",
"Byunggook Na",
"Gunhee Kim",
"Hyuntae Cho",
"Seung-Jin Kang",
"Hee Sun Lee",
"Saerom Choi",
"Heejae Kim",
"Seungwon Lee",
"Yongdeok Kim"
] |
Training configurations from the SAIT_semiconductors_ACS_2023_HfO dataset. This dataset contains HfO configurations from the SAIT semiconductors datasets. SAIT semiconductors datasets comprise two rich datasets for the important semiconductor thin film materials silicon nitride (SiN) and hafnium oxide (HfO), gathered for the development of MLFFs. DFT simulations were conducted under various conditions that include differing initial structures, stoichiometry, temperature, strain, and defects.
|
[
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MD_3220989785209901592711512
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[
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[
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"DS_ppk44zeithjj_0"
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CO_1000448775142580160476625
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SAIT_semiconductors_ACS_2023_HfO_train
|
[
"Geonu Kim",
"Byunggook Na",
"Gunhee Kim",
"Hyuntae Cho",
"Seung-Jin Kang",
"Hee Sun Lee",
"Saerom Choi",
"Heejae Kim",
"Seungwon Lee",
"Yongdeok Kim"
] |
Training configurations from the SAIT_semiconductors_ACS_2023_HfO dataset. This dataset contains HfO configurations from the SAIT semiconductors datasets. SAIT semiconductors datasets comprise two rich datasets for the important semiconductor thin film materials silicon nitride (SiN) and hafnium oxide (HfO), gathered for the development of MLFFs. DFT simulations were conducted under various conditions that include differing initial structures, stoichiometry, temperature, strain, and defects.
|
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DS_ppk44zeithjj_0
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SAIT_semiconductors_ACS_2023_HfO_train__Kim-Na-Kim-Cho-Kang-Lee-Choi-Kim-Lee-Kim__DS_ppk44zeithjj_0
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{"input": {"ediff": {"value": 0.002, "units": "eV"}, "ediffg": {"value": 0.05, "units": "eV/angstrom"}}, "hash": "3220989785209901592711512060749399143158255874818296585998205381974360544737732089417139158924615074837033519915139922028460460046863434186179457886861933", "id": "MD_3220989785209901592711512"}
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MD_3220989785209901592711512
| 2024-08-16T15:17:48 |
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PO_4006925678194433292574032
| null | null | null |
[
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] |
[
"DS_ekrypue10aay_0",
"DS_ppk44zeithjj_0"
] | 2024-01-10T14:13:28 |
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CO_1000543838991849857471431
|
SAIT_semiconductors_ACS_2023_HfO_train
|
[
"Geonu Kim",
"Byunggook Na",
"Gunhee Kim",
"Hyuntae Cho",
"Seung-Jin Kang",
"Hee Sun Lee",
"Saerom Choi",
"Heejae Kim",
"Seungwon Lee",
"Yongdeok Kim"
] |
Training configurations from the SAIT_semiconductors_ACS_2023_HfO dataset. This dataset contains HfO configurations from the SAIT semiconductors datasets. SAIT semiconductors datasets comprise two rich datasets for the important semiconductor thin film materials silicon nitride (SiN) and hafnium oxide (HfO), gathered for the development of MLFFs. DFT simulations were conducted under various conditions that include differing initial structures, stoichiometry, temperature, strain, and defects.
|
[
"Hf",
"O"
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CC-BY-4.0
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{'source-publication': 'https://openreview.net/forum?id=hr9Bd1A9Un', 'source-data': 'https://github.com/SAITPublic/MLFF-Framework', 'other': None}
|
10.60732/495b736b
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|
DS_ppk44zeithjj_0
|
SAIT_semiconductors_ACS_2023_HfO_train__Kim-Na-Kim-Cho-Kang-Lee-Choi-Kim-Lee-Kim__DS_ppk44zeithjj_0
|
Hf32O64
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HfO2
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A2B
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[
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SAIT_semiconductors_ACS_2023_HfO_train
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[
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Training configurations from the SAIT_semiconductors_ACS_2023_HfO dataset. This dataset contains HfO configurations from the SAIT semiconductors datasets. SAIT semiconductors datasets comprise two rich datasets for the important semiconductor thin film materials silicon nitride (SiN) and hafnium oxide (HfO), gathered for the development of MLFFs. DFT simulations were conducted under various conditions that include differing initial structures, stoichiometry, temperature, strain, and defects.
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{'source-publication': 'https://openreview.net/forum?id=hr9Bd1A9Un', 'source-data': 'https://github.com/SAITPublic/MLFF-Framework', 'other': None}
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10.60732/495b736b
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[
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SAIT_semiconductors_ACS_2023_HfO_train
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[
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"Hee Sun Lee",
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Training configurations from the SAIT_semiconductors_ACS_2023_HfO dataset. This dataset contains HfO configurations from the SAIT semiconductors datasets. SAIT semiconductors datasets comprise two rich datasets for the important semiconductor thin film materials silicon nitride (SiN) and hafnium oxide (HfO), gathered for the development of MLFFs. DFT simulations were conducted under various conditions that include differing initial structures, stoichiometry, temperature, strain, and defects.
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{'source-publication': 'https://openreview.net/forum?id=hr9Bd1A9Un', 'source-data': 'https://github.com/SAITPublic/MLFF-Framework', 'other': None}
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10.60732/495b736b
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[
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[
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Training configurations from the SAIT_semiconductors_ACS_2023_HfO dataset. This dataset contains HfO configurations from the SAIT semiconductors datasets. SAIT semiconductors datasets comprise two rich datasets for the important semiconductor thin film materials silicon nitride (SiN) and hafnium oxide (HfO), gathered for the development of MLFFs. DFT simulations were conducted under various conditions that include differing initial structures, stoichiometry, temperature, strain, and defects.
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{'source-publication': 'https://openreview.net/forum?id=hr9Bd1A9Un', 'source-data': 'https://github.com/SAITPublic/MLFF-Framework', 'other': None}
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[
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SAIT_semiconductors_ACS_2023_HfO_train
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[
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"Hee Sun Lee",
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Training configurations from the SAIT_semiconductors_ACS_2023_HfO dataset. This dataset contains HfO configurations from the SAIT semiconductors datasets. SAIT semiconductors datasets comprise two rich datasets for the important semiconductor thin film materials silicon nitride (SiN) and hafnium oxide (HfO), gathered for the development of MLFFs. DFT simulations were conducted under various conditions that include differing initial structures, stoichiometry, temperature, strain, and defects.
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{'source-publication': 'https://openreview.net/forum?id=hr9Bd1A9Un', 'source-data': 'https://github.com/SAITPublic/MLFF-Framework', 'other': None}
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[
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SAIT_semiconductors_ACS_2023_HfO_train
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[
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"Seungwon Lee",
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Training configurations from the SAIT_semiconductors_ACS_2023_HfO dataset. This dataset contains HfO configurations from the SAIT semiconductors datasets. SAIT semiconductors datasets comprise two rich datasets for the important semiconductor thin film materials silicon nitride (SiN) and hafnium oxide (HfO), gathered for the development of MLFFs. DFT simulations were conducted under various conditions that include differing initial structures, stoichiometry, temperature, strain, and defects.
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{'source-publication': 'https://openreview.net/forum?id=hr9Bd1A9Un', 'source-data': 'https://github.com/SAITPublic/MLFF-Framework', 'other': None}
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[
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[
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Training configurations from the SAIT_semiconductors_ACS_2023_HfO dataset. This dataset contains HfO configurations from the SAIT semiconductors datasets. SAIT semiconductors datasets comprise two rich datasets for the important semiconductor thin film materials silicon nitride (SiN) and hafnium oxide (HfO), gathered for the development of MLFFs. DFT simulations were conducted under various conditions that include differing initial structures, stoichiometry, temperature, strain, and defects.
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{'source-publication': 'https://openreview.net/forum?id=hr9Bd1A9Un', 'source-data': 'https://github.com/SAITPublic/MLFF-Framework', 'other': None}
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MD_3220989785209901592711512
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[
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SAIT_semiconductors_ACS_2023_HfO_train
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[
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Training configurations from the SAIT_semiconductors_ACS_2023_HfO dataset. This dataset contains HfO configurations from the SAIT semiconductors datasets. SAIT semiconductors datasets comprise two rich datasets for the important semiconductor thin film materials silicon nitride (SiN) and hafnium oxide (HfO), gathered for the development of MLFFs. DFT simulations were conducted under various conditions that include differing initial structures, stoichiometry, temperature, strain, and defects.
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{'source-publication': 'https://openreview.net/forum?id=hr9Bd1A9Un', 'source-data': 'https://github.com/SAITPublic/MLFF-Framework', 'other': None}
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[
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[
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Training configurations from the SAIT_semiconductors_ACS_2023_HfO dataset. This dataset contains HfO configurations from the SAIT semiconductors datasets. SAIT semiconductors datasets comprise two rich datasets for the important semiconductor thin film materials silicon nitride (SiN) and hafnium oxide (HfO), gathered for the development of MLFFs. DFT simulations were conducted under various conditions that include differing initial structures, stoichiometry, temperature, strain, and defects.
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[
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Training configurations from the SAIT_semiconductors_ACS_2023_HfO dataset. This dataset contains HfO configurations from the SAIT semiconductors datasets. SAIT semiconductors datasets comprise two rich datasets for the important semiconductor thin film materials silicon nitride (SiN) and hafnium oxide (HfO), gathered for the development of MLFFs. DFT simulations were conducted under various conditions that include differing initial structures, stoichiometry, temperature, strain, and defects.
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MD_3220989785209901592711512
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[
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[
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SAIT_semiconductors_ACS_2023_HfO_train
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[
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"Hyuntae Cho",
"Seung-Jin Kang",
"Hee Sun Lee",
"Saerom Choi",
"Heejae Kim",
"Seungwon Lee",
"Yongdeok Kim"
] |
Training configurations from the SAIT_semiconductors_ACS_2023_HfO dataset. This dataset contains HfO configurations from the SAIT semiconductors datasets. SAIT semiconductors datasets comprise two rich datasets for the important semiconductor thin film materials silicon nitride (SiN) and hafnium oxide (HfO), gathered for the development of MLFFs. DFT simulations were conducted under various conditions that include differing initial structures, stoichiometry, temperature, strain, and defects.
|
[
"Hf",
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{'source-publication': 'https://openreview.net/forum?id=hr9Bd1A9Un', 'source-data': 'https://github.com/SAITPublic/MLFF-Framework', 'other': None}
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10.60732/495b736b
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DS_ppk44zeithjj_0
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SAIT_semiconductors_ACS_2023_HfO_train__Kim-Na-Kim-Cho-Kang-Lee-Choi-Kim-Lee-Kim__DS_ppk44zeithjj_0
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Hf32O64
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HfO2
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A2B
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DFT-PBE
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[
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SAIT_semiconductors_ACS_2023_HfO_train
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[
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"Byunggook Na",
"Gunhee Kim",
"Hyuntae Cho",
"Seung-Jin Kang",
"Hee Sun Lee",
"Saerom Choi",
"Heejae Kim",
"Seungwon Lee",
"Yongdeok Kim"
] |
Training configurations from the SAIT_semiconductors_ACS_2023_HfO dataset. This dataset contains HfO configurations from the SAIT semiconductors datasets. SAIT semiconductors datasets comprise two rich datasets for the important semiconductor thin film materials silicon nitride (SiN) and hafnium oxide (HfO), gathered for the development of MLFFs. DFT simulations were conducted under various conditions that include differing initial structures, stoichiometry, temperature, strain, and defects.
|
[
"Hf",
"O"
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{'source-publication': 'https://openreview.net/forum?id=hr9Bd1A9Un', 'source-data': 'https://github.com/SAITPublic/MLFF-Framework', 'other': None}
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10.60732/495b736b
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DS_ppk44zeithjj_0
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SAIT_semiconductors_ACS_2023_HfO_train__Kim-Na-Kim-Cho-Kang-Lee-Choi-Kim-Lee-Kim__DS_ppk44zeithjj_0
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Hf32O64
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HfO2
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[
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SAIT_semiconductors_ACS_2023_HfO_train
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[
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"Byunggook Na",
"Gunhee Kim",
"Hyuntae Cho",
"Seung-Jin Kang",
"Hee Sun Lee",
"Saerom Choi",
"Heejae Kim",
"Seungwon Lee",
"Yongdeok Kim"
] |
Training configurations from the SAIT_semiconductors_ACS_2023_HfO dataset. This dataset contains HfO configurations from the SAIT semiconductors datasets. SAIT semiconductors datasets comprise two rich datasets for the important semiconductor thin film materials silicon nitride (SiN) and hafnium oxide (HfO), gathered for the development of MLFFs. DFT simulations were conducted under various conditions that include differing initial structures, stoichiometry, temperature, strain, and defects.
|
[
"Hf",
"O"
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{'source-publication': 'https://openreview.net/forum?id=hr9Bd1A9Un', 'source-data': 'https://github.com/SAITPublic/MLFF-Framework', 'other': None}
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10.60732/495b736b
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DS_ppk44zeithjj_0
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SAIT_semiconductors_ACS_2023_HfO_train__Kim-Na-Kim-Cho-Kang-Lee-Choi-Kim-Lee-Kim__DS_ppk44zeithjj_0
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Hf32O64
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HfO2
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0.28600349,
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] | null |
[
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],
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] | true | null | null | -869.922329 | null | 4.277565 | 16.770377 |
{"input": {"ediff": {"value": 0.002, "units": "eV"}, "ediffg": {"value": 0.05, "units": "eV/angstrom"}}, "hash": "3220989785209901592711512060749399143158255874818296585998205381974360544737732089417139158924615074837033519915139922028460460046863434186179457886861933", "id": "MD_3220989785209901592711512"}
|
MD_3220989785209901592711512
| 2024-08-16T15:15:47 |
4766590696571942614677122727192159954878620980167125689267996065165138104236192498911030426926639574674048460926940414063939930335304638298445887823246466
|
PO_4766590696571942614677122
| null | null | null |
[
"sait_semiconductors__HfO__Trainset_8858",
"sait_semiconductors__HfO_raw__Crystal__4_1776"
] |
[
"DS_ekrypue10aay_0",
"DS_ppk44zeithjj_0"
] | 2024-01-10T14:14:01 |
10017876211197228962879338087204417333807533890172014320799155842643265473147969348477733729246675827732522269021057304837740127355452163816133272907657319
|
CO_1001787621119722896287933
|
SAIT_semiconductors_ACS_2023_HfO_train
|
[
"Geonu Kim",
"Byunggook Na",
"Gunhee Kim",
"Hyuntae Cho",
"Seung-Jin Kang",
"Hee Sun Lee",
"Saerom Choi",
"Heejae Kim",
"Seungwon Lee",
"Yongdeok Kim"
] |
Training configurations from the SAIT_semiconductors_ACS_2023_HfO dataset. This dataset contains HfO configurations from the SAIT semiconductors datasets. SAIT semiconductors datasets comprise two rich datasets for the important semiconductor thin film materials silicon nitride (SiN) and hafnium oxide (HfO), gathered for the development of MLFFs. DFT simulations were conducted under various conditions that include differing initial structures, stoichiometry, temperature, strain, and defects.
|
[
"Hf",
"O"
] | 2 | 27,958 | 27,960 | 2,684,160 | 0 | 27,958 | 0 | 27,958 | 0 | 27,958 | -907.658156 | 1,515.974108 | 0 | 2024-01-10T08:52:58 |
[
[
1,
1,
1
]
] |
[
3
] |
2024
|
[
0.3333333333333333,
0.6666666666666666
] |
CC-BY-4.0
|
{'source-publication': 'https://openreview.net/forum?id=hr9Bd1A9Un', 'source-data': 'https://github.com/SAITPublic/MLFF-Framework', 'other': None}
|
10.60732/495b736b
|
3293596282163900407733613836674191844225928959401207249304787244757835115614447973365867873328790383706961437530213270207981775875289526860871211073534649
|
DS_ppk44zeithjj_0
|
SAIT_semiconductors_ACS_2023_HfO_train__Kim-Na-Kim-Cho-Kang-Lee-Choi-Kim-Lee-Kim__DS_ppk44zeithjj_0
|
End of preview. Expand
in Data Studio
Dataset
SAIT semiconductors ACS 2023 HfO train
Description
Training configurations from the SAIT_semiconductors_ACS_2023_HfO dataset. This dataset contains HfO configurations from the SAIT semiconductors datasets. SAIT semiconductors datasets comprise two rich datasets for the important semiconductor thin film materials silicon nitride (SiN) and hafnium oxide (HfO), gathered for the development of MLFFs. DFT simulations were conducted under various conditions that include differing initial structures, stoichiometry, temperature, strain, and defects.
Additional details stored in dataset columns prepended with "dataset_".
Dataset authors
Geonu Kim, Byunggook Na, Gunhee Kim, Hyuntae Cho, Seung-Jin Kang, Hee Sun Lee, Saerom Choi, Heejae Kim, Seungwon Lee, Yongdeok Kim
Publication
https://openreview.net/forum?id=hr9Bd1A9Un
Original data link
https://github.com/SAITPublic/MLFF-Framework
License
CC-BY-4.0
Number of unique molecular configurations
27960
Number of atoms
2684160
Elements included
Hf, O
Properties included
energy, atomic forces, cauchy stress
Cite this dataset
Kim, G., Na, B., Kim, G., Cho, H., Kang, S., Lee, H. S., Choi, S., Kim, H., Lee, S., and Kim, Y. SAIT semiconductors ACS 2023 HfO train. ColabFit, 2024. https://doi.org/10.60732/495b736b
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