CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties
Data types to include: formation energy, bandgaps, band offsets, work function, surface energy, defect formation energy, IV-curves, STEM images, electrical measurements, surface roughness, thermal properties, phonons
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[ "006:55" ] |
| contactPoint |
{ "fn": "Daniel Wines", "hasEmail": "mailto:daniel.wines@nist.gov" } |
| description | Data types to include: formation energy, bandgaps, band offsets, work function, surface energy, defect formation energy, IV-curves, STEM images, electrical measurements, surface roughness, thermal properties, phonons |
| distribution |
[ { "title": "CHIPS-FF GitHub", "accessURL": "https://github.com/usnistgov/chipsff" } ] |
| identifier | ark:/88434/mds2-3691 |
| issued | 2025-03-20 |
| keyword |
[ "Density functional theory", "defects", "force-field", "interfaces", "machine learning", "semiconductors" ] |
| landingPage | https://data.nist.gov/od/id/mds2-3691 |
| language |
[ "en" ] |
| license | https://www.nist.gov/open/license |
| modified | 2025-01-14 00:00:00 |
| programCode |
[ "006:045" ] |
| publisher |
{ "name": "National Institute of Standards and Technology", "@type": "org:Organization" } |
| references |
[ "https://doi.org/10.48550/arXiv.2412.10516" ] |
| theme |
[ "Chemistry:Theoretical chemistry and modeling", "Materials:Modeling and computational material science", "Physics:Atomic, molecular, and quantum", "Physics:Condensed matter" ] |
| title | CHIPS-FF: Evaluating Universal Machine Learning Force Fields for Material Properties |
