ANI-Al -- ColabFit
Collection
Datasets from ANI-Al in ColabFit format • 2 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_1001077660686322996544987 | 10010776606863229965449879659844005351707534903347485372980931586082777695802794984085152265933561859528245130267699141454832594187077802176801693300721353 | 2025-04-16T17:25:18 | DS_tz8gsj3xi2g4_0 | 1 | Quantum ESPRESSO | DFT-PBE | -390.326853 | [
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... | [] | null | null | null | null | null | null | 0.121514 | 0.038485 | null | CO_1095556163833740644950141 | 1095556163833740644950141828331197421362043839651913881896937322896784783199823394809999362563053581406984205357739833865238665057457561908535375221226671 | 6381345874293332590608299374421915256567287051909084670062484553354766048560233141017816288998692502015076341987901979405001067735970434805612325301489045 | [
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PO_1001374180758802690192071 | 10013741807588026901920713570639765174749363520948828640239906900915551883293251165341754359385619121267520744091660957224983772351735365599191429471726902 | 2025-04-16T18:59:53 | DS_tz8gsj3xi2g4_0 | 1 | Quantum ESPRESSO | DFT-PBE | -245.563756 | [
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PO_1001486639067864880701084 | 10014866390678648807010845109184580316542343330039943876654318679238269169860014548017097681138779735335101790501483254121128992077858236467059857722059559 | 2025-04-16T20:42:26 | DS_tz8gsj3xi2g4_0 | 1 | Quantum ESPRESSO | DFT-PBE | -221.952197 | [
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PO_1000007687139108265824997 | 10000076871391082658249978039954253621859604466917607016028174581436428872421668830034712029155982233074701326496987309053236339728095107243074464946444903 | 2025-04-16T21:44:51 | DS_tz8gsj3xi2g4_0 | 1 | Quantum ESPRESSO | DFT-PBE | -240.844883 | [
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PO_1000290114613946657722146 | 10002901146139466577221465547490249026695730233751753280606995000526333563245403486879306772194907964772296623878340751652740004152583118462181036316648817 | 2025-04-16T20:09:21 | DS_tz8gsj3xi2g4_0 | 1 | Quantum ESPRESSO | DFT-PBE | -261.700201 | [
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PO_1000678680341864481116974 | 10006786803418644811169749232458392587074999237132035344645742673238812076162685092828609761618158223716724732847123488441389075243132624397269494254831475 | 2025-04-16T15:45:01 | DS_tz8gsj3xi2g4_0 | 1 | Quantum ESPRESSO | DFT-PBE | -371.339518 | [
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PO_1000698294153809273691798 | 10006982941538092736917987822017538378523480242156907507242367212034157635259137562453894954516112463449575252182055934112040467684317367838795668723755524 | 2025-04-16T18:10:42 | DS_tz8gsj3xi2g4_0 | 1 | Quantum ESPRESSO | DFT-PBE | -342.597483 | [
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End of preview. Expand in Data Studio
Cite this dataset
Smith, J. S., Nebgen, B., Mathew, N., Chen, J., Lubbers, N., Burakovsky, L., Tretiak, S., Nam, H. A., Germann, T., Fensin, S., and Barros, K. ANI-Al NC2021-train. ColabFit, 2023. https://doi.org/10.60732/af254882
Cite this dataset
Smith, J. S., Nebgen, B., Mathew, N., Chen, J., Lubbers, N., Burakovsky, L., Tretiak, S., Nam, H. A., Germann, T., Fensin, S., and Barros, K. ANI-Al NC2021-train. ColabFit, 2023. https://doi.org/10.60732/af254882This 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_tz8gsj3xi2g4_0
Visit the ColabFit Exchange to search additional datasets by author, description, element content and more.
https://materials.colabfit.org
Dataset Name
ANI-Al NC2021-train
Description
Approximately 2800 configurations from a train dataset–one of a pair of train/test datasets of aluminum in crystal and melt phases, used for training and testing an ANI neural network model.
Dataset authors
Justin S. Smith, Benjamin Nebgen, Nithin Mathew, Jie Chen, Nicholas Lubbers, Leonid Burakovsky, Sergei Tretiak, Hai Ah Nam, Timothy Germann, Saryu Fensin, Kipton Barros
Publication
https://doi.org/10.1038/s41467-021-21376-0
Original data link
https://github.com/atomistic-ml/ani-al
License
CC-BY-4.0
Number of unique molecular configurations
2779
Number of atoms
363129
Elements included
Al
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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