{"@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": [{"accessURL": "https://github.com/usnistgov/chipsff", "title": "CHIPS-FF GitHub"}], "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": {"@type": "org:Organization", "name": "National Institute of Standards and Technology"}, "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"}