SiH-GAP -- ColabFit
Collection
Datasets from SiH GAP (https://doi.org/10.1103/PhysRevMaterials.6.065603) in ColabFit format • 3 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 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
PO_1001602134991212754755606 | 10016021349912127547556063763298866726652518536883148242537395519866162902162868747792816691260531782915183692236123847096897098372640801878083588086566799 | 2025-04-16T18:10:42 | DS_8arvjtldu4fw_0 | 1 | Quantum ESPRESSO | DFT-PBE | -23,075.41866 | [
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PO_4921110044302656655635827 | 4921110044302656655635827782898676762074171532823223792619147537294810962047264404728620171988919293305397495059217593365891445762101531026164921774135086 | 2025-04-16T18:11:19 | DS_8arvjtldu4fw_0 | 1 | Quantum ESPRESSO | DFT-PBE | -13,752.029099 | [
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... | 144 | 2 | 3 | [
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PO_4932130883239868035483111 | 4932130883239868035483111186169469205507751155002068525483851636907854038817467155734724280900654047978385708026703119493921924954626366929633918087163045 | 2025-04-16T21:12:44 | DS_8arvjtldu4fw_0 | 1 | Quantum ESPRESSO | DFT-PBE | -23,349.333871 | [
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PO_1051970393092161578318111 | "105197039309216157831811119524215415475594638330964746052386601422554417747849600736363323524702553(...TRUNCATED) | 2025-04-17T17:56:16 | DS_8arvjtldu4fw_0 | 1 | Quantum ESPRESSO | DFT-PBE | -6,957.422776 | [[0.7808377437446072,-0.9148175277564596,-0.2990905112400577],[-0.8027530741619533,-1.26514528292696(...TRUNCATED) | [[-3.166983125596008,-0.6590544709341971,0.12074280383527275],[-0.6590544709341972,-12.7157265289021(...TRUNCATED) | true | null | null | null | null | null | 6.436452 | 1.711835 | null | CO_8017203646738729258916289 | "801720364673872925891628962392310209249677824636134286186713569678790980069643216012195990459679942(...TRUNCATED) | "962028755427062213792568246129777350160785348623337970524626968076281457024726131523311072818571962(...TRUNCATED) | [
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PO_1053280282138495208024110 | "105328028213849520802411088051157616828513590052168517687873033403762825446076062850696662240450675(...TRUNCATED) | 2025-04-16T21:12:08 | DS_8arvjtldu4fw_0 | 1 | Quantum ESPRESSO | DFT-PBE | -13,751.260376 | [[-0.16593168115692858,-0.15659162686042666,-0.12264018110787128],[-0.23648486802604804,0.7698151581(...TRUNCATED) | [[-14.084139458945195,1.2329330049748224,-0.88718988717765],[1.2329330049748224,10.020026961065222,-(...TRUNCATED) | true | null | null | null | null | null | 2.658383 | 0.97437 | null | CO_9861860629110800630488596 | "986186062911080063048859691015859143676062653080723770623624652940105465658473818389638268163972068(...TRUNCATED) | "111328119941670937804591640609791656614976197763921246633734263742659947800363226120282681834795257(...TRUNCATED) | [
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0.875
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End of preview. Expand in Data Studio
Cite this dataset
Unruh, D., Meidanshahi, R. V., Goodnick, S. M., Csányi, G., and Zimányi, G. T. Si-H-GAP training. ColabFit, 2023. https://doi.org/10.60732/43a0cef7
Cite this dataset
Unruh, D., Meidanshahi, R. V., Goodnick, S. M., Csányi, G., and Zimányi, G. T. Si-H-GAP training. ColabFit, 2023. https://doi.org/10.60732/43a0cef7This 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_8arvjtldu4fw_0
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Dataset Name
Si-H-GAP training
Description
A set of training configurations of hydrogenated liquid and amorphous silicon from the datasets for Si-H-GAP. Includes virial sigmas used for configurations used in the corresponding publication (virial-sigma-paper) as well as an alternate configuration defined by doubled virial sigma prefactors (from 0.025 to 0.05).
Dataset authors
Davis Unruh, Reza Vatan Meidanshahi, Stephen M. Goodnick, Gábor Csányi, Gergely T. Zimányi
Publication
https://doi.org/10.1103/PhysRevMaterials.6.065603
Original data link
https://github.com/dgunruh/Si-H-GAP
License
CC-BY-4.0
Number of unique molecular configurations
392
Number of atoms
65909
Elements included
H, Si
Properties included
energy, atomic forces, cauchy stress
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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Size of downloaded dataset files:
3.68 MB
Size of the auto-converted Parquet files:
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Number of rows:
393