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
string | atomization_energy
float64 | cauchy_stress
string | 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
C19H23NO3
|
C19H23NO3
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A23B19C3D
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| 1 |
ORCA 4.2.1
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DFT-B973c
| null |
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| null |
[]
| null | null | null | -27,692.757429 | null | 3.72009 | 1.609828 |
{"basis-set": "def2m-TZVP", "input": "! b97-3c engrad ScfConvForced tightscf\n%elprop dipole true quadrupole true end\n%output PrintLevel mini Print[P DFTD GRAD] 1 end", "hash": "4168067693972645748256367256168796659343092765014357661480771847566230807791481219965080394505736503142715286215810344413004037401697763538007633612180186", "id": "MD_4168067693972645748256367"}
|
MD_4168067693972645748256367
| 2025-04-16T22:36:58 |
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|
PO_1302541622466170000524091
|
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|
MD_4915551579020556885655598
| null |
[
"COMP6v2-wB97MV-def2TZVPP__natoms_046__ix_319",
"COMP6v2-B973c-def2mTZVP__natoms_046__ix_317"
] |
[
"DS_m6etjqhlu40m_0",
"DS_0dbwoxq96gga_0"
] | 2024-04-11T00:13:18 |
3208796107674411275652010807160126788645736021467585331427554056956682144860396296643498354563946371504381542911890026630968492211795416731406685910497331
|
CO_3208796107674411275652010
|
COMP6v2-B973c-def2mTZVP
|
[
"Kate Huddleston",
"Roman Zubatyuk",
"Justin Smith",
"Adrian Roitberg",
"Olexandr Isayev",
"Ignacio Pickering",
"Christian Devereux",
"Kipton Barros"
] |
COMP6v2-B973c-def2mTZVP is the portion of COMP6v2 calculated at the B973c/def2mTZVP level of theory. COmprehensive Machine-learning Potential (COMP6) Benchmark Suite version 2.0 is an extension of the COMP6 benchmark found in the following repository: https://github.com/isayev/COMP6. COMP6v2 is a data set of density functional properties for molecules containing H, C, N, O, S, F, and Cl. It is available at the following levels of theory: wB97X/631Gd (data used to train model in the ANI-2x paper); wB97MD3BJ/def2TZVPP; wB97MV/def2TZVPP; B973c/def2mTZVP. The 6 subsets from COMP6 (ANI-MD, DrugBank, GDB07to09, GDB10to13 Tripeptides, and s66x8) are contained in each of the COMP6v2 datasets corresponding to the above levels of theory.
|
[
"N",
"C",
"Cl",
"H",
"F",
"S",
"O"
] | 7 | 156,317 | 156,330 | 3,786,071 | 0 | 156,317 | 0 | 156,317 | 0 | 156,317 | -20,184.084152 | 137,771,246.995456 | 0 | 2024-04-10T20:52:54 |
[
[
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0
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[
0
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2023
|
[
0.06926996350570287,
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0.00636385318711667,
0.5014966702948782,
0.006088897963086271,
0.013470428842987889,
0.06145922778521586
] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1021/acs.jctc.0c00121', 'source-data': 'https://doi.org/10.5281/zenodo.10126157', 'other': None}
|
10.60732/2228cf4a
|
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|
DS_0dbwoxq96gga_0
|
COMP6v2-B973c-def2mTZVP__Huddleston-Zubatyuk-Smith-Roitberg-Isayev-Pickering-Devereux-Barros__DS_0dbwoxq96gga_0
|
C5H5ClO2
|
C5ClH5O2
|
A5B5C2D
|
[
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[
"C",
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"H",
"O"
] |
[
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] | 4 | 13 |
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[]
| null | null | null | -21,874.36986 | null | 5.040917 | 2.407863 |
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[
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[
"DS_m6etjqhlu40m_0",
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CO_1226189101784110714896714
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COMP6v2-B973c-def2mTZVP
|
[
"Kate Huddleston",
"Roman Zubatyuk",
"Justin Smith",
"Adrian Roitberg",
"Olexandr Isayev",
"Ignacio Pickering",
"Christian Devereux",
"Kipton Barros"
] |
COMP6v2-B973c-def2mTZVP is the portion of COMP6v2 calculated at the B973c/def2mTZVP level of theory. COmprehensive Machine-learning Potential (COMP6) Benchmark Suite version 2.0 is an extension of the COMP6 benchmark found in the following repository: https://github.com/isayev/COMP6. COMP6v2 is a data set of density functional properties for molecules containing H, C, N, O, S, F, and Cl. It is available at the following levels of theory: wB97X/631Gd (data used to train model in the ANI-2x paper); wB97MD3BJ/def2TZVPP; wB97MV/def2TZVPP; B973c/def2mTZVP. The 6 subsets from COMP6 (ANI-MD, DrugBank, GDB07to09, GDB10to13 Tripeptides, and s66x8) are contained in each of the COMP6v2 datasets corresponding to the above levels of theory.
|
[
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"O"
] | 7 | 156,317 | 156,330 | 3,786,071 | 0 | 156,317 | 0 | 156,317 | 0 | 156,317 | -20,184.084152 | 137,771,246.995456 | 0 | 2024-04-10T20:52:54 |
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{'source-publication': 'https://doi.org/10.1021/acs.jctc.0c00121', 'source-data': 'https://doi.org/10.5281/zenodo.10126157', 'other': None}
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10.60732/2228cf4a
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| null |
[]
| null | null | null | -26,348.407588 | null | 1.812736 | 0.769462 |
{"basis-set": "def2m-TZVP", "input": "! b97-3c engrad ScfConvForced tightscf\n%elprop dipole true quadrupole true end\n%output PrintLevel mini Print[P DFTD GRAD] 1 end", "hash": "4168067693972645748256367256168796659343092765014357661480771847566230807791481219965080394505736503142715286215810344413004037401697763538007633612180186", "id": "MD_4168067693972645748256367"}
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MD_4168067693972645748256367
| 2025-04-16T22:35:01 |
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[
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[
"DS_m6etjqhlu40m_0",
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COMP6v2-B973c-def2mTZVP
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[
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"Justin Smith",
"Adrian Roitberg",
"Olexandr Isayev",
"Ignacio Pickering",
"Christian Devereux",
"Kipton Barros"
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[]
| null | null | null | -21,356.307835 | null | 3.562159 | 1.899304 |
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MD_4168067693972645748256367
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[
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[
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CO_2765580669230158705247708
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COMP6v2-B973c-def2mTZVP
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[
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"Justin Smith",
"Adrian Roitberg",
"Olexandr Isayev",
"Ignacio Pickering",
"Christian Devereux",
"Kipton Barros"
] |
COMP6v2-B973c-def2mTZVP is the portion of COMP6v2 calculated at the B973c/def2mTZVP level of theory. COmprehensive Machine-learning Potential (COMP6) Benchmark Suite version 2.0 is an extension of the COMP6 benchmark found in the following repository: https://github.com/isayev/COMP6. COMP6v2 is a data set of density functional properties for molecules containing H, C, N, O, S, F, and Cl. It is available at the following levels of theory: wB97X/631Gd (data used to train model in the ANI-2x paper); wB97MD3BJ/def2TZVPP; wB97MV/def2TZVPP; B973c/def2mTZVP. The 6 subsets from COMP6 (ANI-MD, DrugBank, GDB07to09, GDB10to13 Tripeptides, and s66x8) are contained in each of the COMP6v2 datasets corresponding to the above levels of theory.
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| null | null | null | -8,891.435914 | null | 1.700194 | 1.058767 |
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MD_4168067693972645748256367
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[
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"Christian Devereux",
"Kipton Barros"
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[
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[
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"Adrian Roitberg",
"Olexandr Isayev",
"Ignacio Pickering",
"Christian Devereux",
"Kipton Barros"
] |
COMP6v2-B973c-def2mTZVP is the portion of COMP6v2 calculated at the B973c/def2mTZVP level of theory. COmprehensive Machine-learning Potential (COMP6) Benchmark Suite version 2.0 is an extension of the COMP6 benchmark found in the following repository: https://github.com/isayev/COMP6. COMP6v2 is a data set of density functional properties for molecules containing H, C, N, O, S, F, and Cl. It is available at the following levels of theory: wB97X/631Gd (data used to train model in the ANI-2x paper); wB97MD3BJ/def2TZVPP; wB97MV/def2TZVPP; B973c/def2mTZVP. The 6 subsets from COMP6 (ANI-MD, DrugBank, GDB07to09, GDB10to13 Tripeptides, and s66x8) are contained in each of the COMP6v2 datasets corresponding to the above levels of theory.
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[
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CO_1073372343034211773856373
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COMP6v2-B973c-def2mTZVP
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[
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"Justin Smith",
"Adrian Roitberg",
"Olexandr Isayev",
"Ignacio Pickering",
"Christian Devereux",
"Kipton Barros"
] |
COMP6v2-B973c-def2mTZVP is the portion of COMP6v2 calculated at the B973c/def2mTZVP level of theory. COmprehensive Machine-learning Potential (COMP6) Benchmark Suite version 2.0 is an extension of the COMP6 benchmark found in the following repository: https://github.com/isayev/COMP6. COMP6v2 is a data set of density functional properties for molecules containing H, C, N, O, S, F, and Cl. It is available at the following levels of theory: wB97X/631Gd (data used to train model in the ANI-2x paper); wB97MD3BJ/def2TZVPP; wB97MV/def2TZVPP; B973c/def2mTZVP. The 6 subsets from COMP6 (ANI-MD, DrugBank, GDB07to09, GDB10to13 Tripeptides, and s66x8) are contained in each of the COMP6v2 datasets corresponding to the above levels of theory.
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[]
| null | null | null | -8,692.60731 | null | 1.504311 | 0.773432 |
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[
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COMP6v2-B973c-def2mTZVP
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[
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"Justin Smith",
"Adrian Roitberg",
"Olexandr Isayev",
"Ignacio Pickering",
"Christian Devereux",
"Kipton Barros"
] |
COMP6v2-B973c-def2mTZVP is the portion of COMP6v2 calculated at the B973c/def2mTZVP level of theory. COmprehensive Machine-learning Potential (COMP6) Benchmark Suite version 2.0 is an extension of the COMP6 benchmark found in the following repository: https://github.com/isayev/COMP6. COMP6v2 is a data set of density functional properties for molecules containing H, C, N, O, S, F, and Cl. It is available at the following levels of theory: wB97X/631Gd (data used to train model in the ANI-2x paper); wB97MD3BJ/def2TZVPP; wB97MV/def2TZVPP; B973c/def2mTZVP. The 6 subsets from COMP6 (ANI-MD, DrugBank, GDB07to09, GDB10to13 Tripeptides, and s66x8) are contained in each of the COMP6v2 datasets corresponding to the above levels of theory.
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[
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COMP6v2-B973c-def2mTZVP
|
[
"Kate Huddleston",
"Roman Zubatyuk",
"Justin Smith",
"Adrian Roitberg",
"Olexandr Isayev",
"Ignacio Pickering",
"Christian Devereux",
"Kipton Barros"
] |
COMP6v2-B973c-def2mTZVP is the portion of COMP6v2 calculated at the B973c/def2mTZVP level of theory. COmprehensive Machine-learning Potential (COMP6) Benchmark Suite version 2.0 is an extension of the COMP6 benchmark found in the following repository: https://github.com/isayev/COMP6. COMP6v2 is a data set of density functional properties for molecules containing H, C, N, O, S, F, and Cl. It is available at the following levels of theory: wB97X/631Gd (data used to train model in the ANI-2x paper); wB97MD3BJ/def2TZVPP; wB97MV/def2TZVPP; B973c/def2mTZVP. The 6 subsets from COMP6 (ANI-MD, DrugBank, GDB07to09, GDB10to13 Tripeptides, and s66x8) are contained in each of the COMP6v2 datasets corresponding to the above levels of theory.
|
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[
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"Justin Smith",
"Adrian Roitberg",
"Olexandr Isayev",
"Ignacio Pickering",
"Christian Devereux",
"Kipton Barros"
] |
COMP6v2-B973c-def2mTZVP is the portion of COMP6v2 calculated at the B973c/def2mTZVP level of theory. COmprehensive Machine-learning Potential (COMP6) Benchmark Suite version 2.0 is an extension of the COMP6 benchmark found in the following repository: https://github.com/isayev/COMP6. COMP6v2 is a data set of density functional properties for molecules containing H, C, N, O, S, F, and Cl. It is available at the following levels of theory: wB97X/631Gd (data used to train model in the ANI-2x paper); wB97MD3BJ/def2TZVPP; wB97MV/def2TZVPP; B973c/def2mTZVP. The 6 subsets from COMP6 (ANI-MD, DrugBank, GDB07to09, GDB10to13 Tripeptides, and s66x8) are contained in each of the COMP6v2 datasets corresponding to the above levels of theory.
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[
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[
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COMP6v2-B973c-def2mTZVP
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[
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"Justin Smith",
"Adrian Roitberg",
"Olexandr Isayev",
"Ignacio Pickering",
"Christian Devereux",
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] |
COMP6v2-B973c-def2mTZVP is the portion of COMP6v2 calculated at the B973c/def2mTZVP level of theory. COmprehensive Machine-learning Potential (COMP6) Benchmark Suite version 2.0 is an extension of the COMP6 benchmark found in the following repository: https://github.com/isayev/COMP6. COMP6v2 is a data set of density functional properties for molecules containing H, C, N, O, S, F, and Cl. It is available at the following levels of theory: wB97X/631Gd (data used to train model in the ANI-2x paper); wB97MD3BJ/def2TZVPP; wB97MV/def2TZVPP; B973c/def2mTZVP. The 6 subsets from COMP6 (ANI-MD, DrugBank, GDB07to09, GDB10to13 Tripeptides, and s66x8) are contained in each of the COMP6v2 datasets corresponding to the above levels of theory.
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[
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"Kipton Barros"
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COMP6v2-B973c-def2mTZVP is the portion of COMP6v2 calculated at the B973c/def2mTZVP level of theory. COmprehensive Machine-learning Potential (COMP6) Benchmark Suite version 2.0 is an extension of the COMP6 benchmark found in the following repository: https://github.com/isayev/COMP6. COMP6v2 is a data set of density functional properties for molecules containing H, C, N, O, S, F, and Cl. It is available at the following levels of theory: wB97X/631Gd (data used to train model in the ANI-2x paper); wB97MD3BJ/def2TZVPP; wB97MV/def2TZVPP; B973c/def2mTZVP. The 6 subsets from COMP6 (ANI-MD, DrugBank, GDB07to09, GDB10to13 Tripeptides, and s66x8) are contained in each of the COMP6v2 datasets corresponding to the above levels of theory.
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[
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"Christian Devereux",
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DFT-B973c
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[[-1.3400088878871401, 2.0480411880704206, -0.33215100453849306], [2.8205419658080104, -0.2703881513063423, 0.2401706495510968], [0.7300929130412438, 1.7958622620018074, 2.3772049869015848], [-0.4298458067744884, 1.2572156989667402, 0.2443096247475083], [0.34655240604772697, 1.548470143453348, 0.5584436275030927], [0.30373538542810063, -0.9242811272562493, 0.32623429817414923], [-0.9471328340319036, -0.682714760559365, -2.6281143511781475], [0.17138340388010864, 0.626467313967486, 0.5527730229145913], [-0.9640419089858269, -1.3259294638325532, -0.30050816016367154], [-0.2397424106665139, -0.20338070222340096, 0.23272563367239288], [-1.0310632964867184, -2.3761178916717904, -1.5980649605556967], [0.12546013079257093, -1.991400728777505, -0.9904033600864697], [0.5168661884056349, -0.0012570573086456264, 0.8087446178500012], [-0.06279727391338298, 0.4994131603555649, 0.5086354765783736]]
| null |
[]
| null | null | null | -11,311.099945 | null | 3.067452 | 1.798089 |
{"basis-set": "def2m-TZVP", "input": "! b97-3c engrad ScfConvForced tightscf\n%elprop dipole true quadrupole true end\n%output PrintLevel mini Print[P DFTD GRAD] 1 end", "hash": "4168067693972645748256367256168796659343092765014357661480771847566230807791481219965080394505736503142715286215810344413004037401697763538007633612180186", "id": "MD_4168067693972645748256367"}
|
MD_4168067693972645748256367
| 2025-04-17T22:48:22 |
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|
PO_7250440363209338093788417
|
{"dipole": [0.46045008301734924, 0.3057654798030853, 0.4392246901988983], "force-corrections": [[-0.001834179274737835, 0.00012969122326467186, 2.96024045383092e-05], [-0.001349159050732851, 0.00011836425983346999, 0.0002548433549236506], [-0.0004009032854810357, -0.0014376167673617601, 0.00034226159914396703], [0.0009688008576631546, -0.0016497959150001407, 1.995729144255165e-05], [0.001826272695325315, -0.0005307872779667377, 0.0001341717434115708], [0.001149775111116469, 0.000556542188860476, 0.00019126762344967574], [0.0005574436509050429, 0.0012829018523916602, -0.0002462035627104342], [-0.0003845232422463596, 0.0011795267928391695, 0.000317217200063169], [-0.0006402679719030857, -3.339317481731996e-05, -7.47552840039134e-05], [-0.0005923542194068432, 0.00018505612388253212, -4.1025737118616235e-06], [-0.00013116898480802774, -0.0007447805837728083, -1.941494338097982e-05], [0.0002750765415839851, -0.0005064609576947987, -6.939415470696986e-05], [0.0003572640707716346, 0.0006940209423191845, -0.0007512150914408267], [0.00019792131206486374, 0.000756727356929332, -0.00012423751468304545]], "hash": "6674230187207099530713528001636611418693930773299245401370264684680763821134928669854536080839799629827835562685921884302831281852010208475061536519992231", "id": "MD_6674230187207099530713528"}
|
MD_6674230187207099530713528
| null |
[
"COMP6v2-wB97MV-def2TZVPP__natoms_014__ix_6201",
"COMP6v2-B973c-def2mTZVP__natoms_014__ix_6201"
] |
[
"DS_m6etjqhlu40m_0",
"DS_0dbwoxq96gga_0"
] | 2024-04-11T00:13:13 |
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|
CO_8334255976420733229402001
|
COMP6v2-B973c-def2mTZVP
|
[
"Kate Huddleston",
"Roman Zubatyuk",
"Justin Smith",
"Adrian Roitberg",
"Olexandr Isayev",
"Ignacio Pickering",
"Christian Devereux",
"Kipton Barros"
] |
COMP6v2-B973c-def2mTZVP is the portion of COMP6v2 calculated at the B973c/def2mTZVP level of theory. COmprehensive Machine-learning Potential (COMP6) Benchmark Suite version 2.0 is an extension of the COMP6 benchmark found in the following repository: https://github.com/isayev/COMP6. COMP6v2 is a data set of density functional properties for molecules containing H, C, N, O, S, F, and Cl. It is available at the following levels of theory: wB97X/631Gd (data used to train model in the ANI-2x paper); wB97MD3BJ/def2TZVPP; wB97MV/def2TZVPP; B973c/def2mTZVP. The 6 subsets from COMP6 (ANI-MD, DrugBank, GDB07to09, GDB10to13 Tripeptides, and s66x8) are contained in each of the COMP6v2 datasets corresponding to the above levels of theory.
|
[
"N",
"C",
"Cl",
"H",
"F",
"S",
"O"
] | 7 | 156,317 | 156,330 | 3,786,071 | 0 | 156,317 | 0 | 156,317 | 0 | 156,317 | -20,184.084152 | 137,771,246.995456 | 0 | 2024-04-10T20:52:54 |
[
[
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[
0
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2023
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CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1021/acs.jctc.0c00121', 'source-data': 'https://doi.org/10.5281/zenodo.10126157', 'other': None}
|
10.60732/2228cf4a
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|
DS_0dbwoxq96gga_0
|
COMP6v2-B973c-def2mTZVP__Huddleston-Zubatyuk-Smith-Roitberg-Isayev-Pickering-Devereux-Barros__DS_0dbwoxq96gga_0
|
C15H21N3O
|
C15H21N3O
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A21B15C3D
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| 1 |
ORCA 4.2.1
|
DFT-B973c
| null |
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| null |
[]
| null | null | null | -22,400.991718 | null | 5.0165 | 1.923759 |
{"basis-set": "def2m-TZVP", "input": "! b97-3c engrad ScfConvForced tightscf\n%elprop dipole true quadrupole true end\n%output PrintLevel mini Print[P DFTD GRAD] 1 end", "hash": "4168067693972645748256367256168796659343092765014357661480771847566230807791481219965080394505736503142715286215810344413004037401697763538007633612180186", "id": "MD_4168067693972645748256367"}
|
MD_4168067693972645748256367
| 2025-04-17T22:48:22 |
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|
PO_7750137894748204258733936
|
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|
MD_5877031597256320913780243
| null |
[
"COMP6v2-wB97MV-def2TZVPP__natoms_040__ix_591",
"COMP6v2-B973c-def2mTZVP__natoms_040__ix_591"
] |
[
"DS_m6etjqhlu40m_0",
"DS_0dbwoxq96gga_0"
] | 2024-04-11T00:13:19 |
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|
CO_9111581864326205700425608
|
COMP6v2-B973c-def2mTZVP
|
[
"Kate Huddleston",
"Roman Zubatyuk",
"Justin Smith",
"Adrian Roitberg",
"Olexandr Isayev",
"Ignacio Pickering",
"Christian Devereux",
"Kipton Barros"
] |
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[]
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[
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COMP6v2-B973c-def2mTZVP
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[
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"Adrian Roitberg",
"Olexandr Isayev",
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"Christian Devereux",
"Kipton Barros"
] |
COMP6v2-B973c-def2mTZVP is the portion of COMP6v2 calculated at the B973c/def2mTZVP level of theory. COmprehensive Machine-learning Potential (COMP6) Benchmark Suite version 2.0 is an extension of the COMP6 benchmark found in the following repository: https://github.com/isayev/COMP6. COMP6v2 is a data set of density functional properties for molecules containing H, C, N, O, S, F, and Cl. It is available at the following levels of theory: wB97X/631Gd (data used to train model in the ANI-2x paper); wB97MD3BJ/def2TZVPP; wB97MV/def2TZVPP; B973c/def2mTZVP. The 6 subsets from COMP6 (ANI-MD, DrugBank, GDB07to09, GDB10to13 Tripeptides, and s66x8) are contained in each of the COMP6v2 datasets corresponding to the above levels of theory.
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[
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[
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] | 2024-04-11T00:13:14 |
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CO_4039031508851859854029328
|
COMP6v2-B973c-def2mTZVP
|
[
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"Roman Zubatyuk",
"Justin Smith",
"Adrian Roitberg",
"Olexandr Isayev",
"Ignacio Pickering",
"Christian Devereux",
"Kipton Barros"
] |
COMP6v2-B973c-def2mTZVP is the portion of COMP6v2 calculated at the B973c/def2mTZVP level of theory. COmprehensive Machine-learning Potential (COMP6) Benchmark Suite version 2.0 is an extension of the COMP6 benchmark found in the following repository: https://github.com/isayev/COMP6. COMP6v2 is a data set of density functional properties for molecules containing H, C, N, O, S, F, and Cl. It is available at the following levels of theory: wB97X/631Gd (data used to train model in the ANI-2x paper); wB97MD3BJ/def2TZVPP; wB97MV/def2TZVPP; B973c/def2mTZVP. The 6 subsets from COMP6 (ANI-MD, DrugBank, GDB07to09, GDB10to13 Tripeptides, and s66x8) are contained in each of the COMP6v2 datasets corresponding to the above levels of theory.
|
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0.006088897963086271,
0.013470428842987889,
0.06145922778521586
] |
CC-BY-4.0
|
{'source-publication': 'https://doi.org/10.1021/acs.jctc.0c00121', 'source-data': 'https://doi.org/10.5281/zenodo.10126157', 'other': None}
|
10.60732/2228cf4a
|
8470662353833409913632989817565362005809133474874501771410647006453404313616219583229892930489748003664294884289621916285412650601911576829769669777543168
|
DS_0dbwoxq96gga_0
|
COMP6v2-B973c-def2mTZVP__Huddleston-Zubatyuk-Smith-Roitberg-Isayev-Pickering-Devereux-Barros__DS_0dbwoxq96gga_0
|
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Cite this dataset
Huddleston, K., Zubatyuk, R., Smith, J., Roitberg, A., Isayev, O., Pickering, I., Devereux, C., and Barros, K. COMP6v2-B973c-def2mTZVP. ColabFit, 2023. https://doi.org/10.60732/2228cf4a
View on the ColabFit Exchange
https://materials.colabfit.org/id/DS_0dbwoxq96gga_0
Dataset Name
COMP6v2-B973c-def2mTZVP
Description
COMP6v2-B973c-def2mTZVP is the portion of COMP6v2 calculated at the B973c/def2mTZVP level of theory. COmprehensive Machine-learning Potential (COMP6) Benchmark Suite version 2.0 is an extension of the COMP6 benchmark found in the following repository: https://github.com/isayev/COMP6. COMP6v2 is a data set of density functional properties for molecules containing H, C, N, O, S, F, and Cl. It is available at the following levels of theory: wB97X/631Gd (data used to train model in the ANI-2x paper); wB97MD3BJ/def2TZVPP; wB97MV/def2TZVPP; B973c/def2mTZVP. The 6 subsets from COMP6 (ANI-MD, DrugBank, GDB07to09, GDB10to13 Tripeptides, and s66x8) are contained in each of the COMP6v2 datasets corresponding to the above levels of theory.
Additional details stored in dataset columns prepended with "dataset_".
Dataset authors
Kate Huddleston, Roman Zubatyuk, Justin Smith, Adrian Roitberg, Olexandr Isayev, Ignacio Pickering, Christian Devereux, Kipton Barros
Publication
https://doi.org/10.1021/acs.jctc.0c00121
Original data link
https://doi.org/10.5281/zenodo.10126157
License
CC-BY-4.0
Number of unique molecular configurations
156330
Number of atoms
3786071
Elements included
N, C, Cl, H, F, S, O
Properties included
energy, atomic forces, cauchy stress
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