BOTnet -- ColabFit
Collection
Datasets from BOTnet in ColabFit format • 7 items • Updated
property_id string | property_hash string | last_modified timestamp[us] | dataset_id string | multiplicity int32 | software string | method string | energy float64 | atomic_forces list | cauchy_stress list | cauchy_stress_volume_normalized bool | electronic_band_gap float64 | electronic_band_gap_type string | formation_energy float64 | adsorption_energy float64 | atomization_energy float64 | max_force_norm float64 | mean_force_norm float64 | energy_above_hull float64 | configuration_id string | configuration_hash string | structure_hash string | cell list | positions list | pbc list | chemical_formula_hill string | chemical_formula_reduced string | chemical_formula_anonymous string | elements list | elements_ratios list | atomic_numbers list | nsites int32 | nelements int32 | nperiodic_dimensions int32 | dimension_types list | names list | labels list | property_metadata_path string | configuration_metadata_path string | hash string | configuration_metadata string | property_metadata string |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
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PO_1005605972225487814427932 | 1005605972225487814427932466999820250475755056384841075114216352476871640568044667664616276209326218416358154027449485964655575007165884957701773536944239 | 2025-04-16T21:43:25 | DS_632yputkfhpg_0 | 1 | ORCA 5.0 | DFT-PBE-D3 | -9,390.891555 | [
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PO_4974175073931196198839954 | 497417507393119619883995421863543305078132693593186654357053097914190459650818511595681743102763913996930988015833851652320014182645459147902729574781636 | 2025-04-16T21:28:46 | DS_632yputkfhpg_0 | 1 | ORCA 5.0 | DFT-PBE-D3 | -9,390.805989 | [
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PO_4998717347084990227330349 | 4998717347084990227330349496492933308538063772340502560600141759334921354031142888357029405820881257985927983138925643528821059174525322432311259732158969 | 2025-04-17T17:45:49 | DS_632yputkfhpg_0 | 1 | ORCA 5.0 | DFT-PBE-D3 | -9,391.104183 | [
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PO_1009634836304928908568975 | 10096348363049289085689750399146864221060770096230185101765740932148815181938916810057357379821391848458666621106820728245322085108985591300462876178032657 | 2025-04-16T18:45:49 | DS_632yputkfhpg_0 | 1 | ORCA 5.0 | DFT-PBE-D3 | -9,390.535321 | [
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PO_4444016834470523056447168 | 4444016834470523056447168740569386909335769826842721117148482585179035233964159368781439281433218986680973647496503290219577629313372144813502784234631538 | 2025-04-16T22:04:30 | DS_632yputkfhpg_0 | 1 | ORCA 5.0 | DFT-PBE-D3 | -9,391.012104 | [
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PO_4447844748336664052187433 | 4447844748336664052187433762022568658477326736926152400028934975673840007507313965405198420490257084113809854988375668133620153899188507868787501454532190 | 2025-04-16T18:15:45 | DS_632yputkfhpg_0 | 1 | ORCA 5.0 | DFT-PBE-D3 | -9,391.183188 | [
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PO_1012507950210272704781364 | 10125079502102727047813640190699869009773375538304045653734124116501815888747620177487700119776874228917552256119939938518573668211158313868057773458648427 | 2025-04-16T16:10:44 | DS_632yputkfhpg_0 | 1 | ORCA 5.0 | DFT-PBE-D3 | -9,391.000055 | [
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] | C5H8O2 | C5H8O2 | A8B5C2 | [
"C",
"H",
"O"
] | [
0.3333333333333333,
0.5333333333333333,
0.13333333333333333
] | [
6,
6,
6,
8,
6,
8,
6,
1,
1,
1,
1,
1,
1,
1,
1
] | 15 | 3 | 0 | [
0,
0,
0
] | [
"train_600K_MD_199"
] | null | data/MD/5202/MD_1086845055867124744315202.json | null | null | null | {"hash": "10868450558671247443152026947160338505683745266658651051718065983487878962987857602829315249215796444208488632888003673539585986066311769564391053988452926", "id": "MD_1086845055867124744315202"} |
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Cite this dataset
Batatia, I., Batzner, S., Kovács, D. P., Musaelian, A., Simm, G. N. C., Drautz, R., Ortner, C., Kozinsky, B., and Csányi, G. BOTnet ACAC 2022 train 600K MD. ColabFit, 2023. https://doi.org/10.60732/7239a192
Cite this dataset
Batatia, I., Batzner, S., Kovács, D. P., Musaelian, A., Simm, G. N. C., Drautz, R., Ortner, C., Kozinsky, B., and Csányi, G. BOTnet ACAC 2022 train 600K MD. ColabFit, 2023. https://doi.org/10.60732/7239a192This dataset has been curated and formatted for the ColabFit Exchange
This dataset is also available on the ColabFit Exchange:
https://materials.colabfit.org/id/DS_632yputkfhpg_0
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Dataset Name
BOTnet ACAC 2022 train 600K MD
Description
500 decorrelated geometries sampled from 600 K xTB MD run. Acetylacetone dataset generated from a long molecular dynamics simulation at 300 K using a Langevin thermostat at the semi-empirical GFN2-xTB level of theory. Configurations were sampled at an interval of 1 ps and the resulting set of configurations were recomputed with density functional theory using the PBE exchange-correlation functional with D3 dispersion correction and def2-SVP basis set and VeryTightSCF convergence settings using the ORCA electronic structure package.
Dataset authors
Ilyes Batatia, Simon Batzner, Dávid Péter Kovács, Albert Musaelian, Gregor N. C. Simm, Ralf Drautz, Christoph Ortner, Boris Kozinsky, Gábor Csányi
Publication
https://doi.org/10.1038/s42256-024-00956-x
Original data link
https://github.com/davkovacs/BOTNet-datasets
License
MIT
Number of unique molecular configurations
500
Number of atoms
7500
Elements included
C, H, O
Properties included
energy, atomic forces
Usage
ds.parquet: Aggregated dataset information.co/directory: Configuration rows each include a structure, calculated properties, and metadata.cs/directory : Configuration sets are subsets of configurations grouped by some common characteristic. Ifcs/does not exist, no configurations sets have been defined for this dataset.cs_co_map/directory : The mapping of configurations to configuration sets (if defined).
ColabFit Exchange documentation includes descriptions of content and example code for parsing parquet files:
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