Machine learning prediction of accurate atomization energies of organic molecules from low-fidelity quantum chemical calculations
Year: 2019
Type: article
Source: MRS Communications
DOI: 10.1557/mrc.2019.107
Language: English
Subfield: Materials Chemistry
Field: Materials Science
Domain: Physical Sciences
Open Access status: green
Funders: National Science Foundation, U.S. Department of Energy, U.S. Department of Commerce, National Institute of Standards and Technology, Office of Science, Basic Energy Sciences, Advanced Scientific Computing Research, Laboratory Directed Research and Development, Center for Hierarchical Materials Design, Argonne National Laboratory
Awards: G102163711, G107546177, G1193633358, G1286251239, G1313983767, G1314375578, G1664757510, G1676583131, G2287611682, G262962599, G3075337988, G3226045097, G3448829509, XSEDE 2.0: Integrating, Enabling and Enhancing National Cyberinfrastructure with Expanding Community Involvement, G3886122786, G3961431766, G426311843, G4382344593, G498139845, G513797252, G5387317844, G5692331544, BD Spokes: SPOKE: MIDWEST: Collaborative: Integrative Materials Design (IMaD): Leverage, Innovate, and Disseminate, ADVANCED MATERIALS CENTER OF EXCELLENCE, G6563589624, G6597714080, G6806689448, G6848031779, G6918803902, G7287433187, G7583137537, G788322952, G878601127, G8927650245, G969889393, G993149817
OpenAlex ID: W2948598948