Tell Machine Learning Potentials What They Are Needed For: Simulation-Oriented Training Exemplified for Glycine

Abstract

Research context

OpenAlex topics: Machine Learning in Materials Science · Advanced Chemical Physics Studies · Spectroscopy and Quantum Chemical Studies

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Affiliations

State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Fujian Provincial Key Laboratory of Theoretical and Computational Chemistry, and Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen University, Xiamen, Fujian 361005, China

Independent Researcher, Toronto, Ontario M9B0E3, Canada

Department of Chemistry and Cherry L. Emerson Center for Scientific Computation, Emory University, Atlanta, Georgia 30322, United States

Department of Physics and Materials Science, University of Luxembourg, Luxembourg City L-1511, Luxembourg

Dipartimento di Chimica, Università degli Studi di Milano, via Golgi 19, 20133 Milano, Italy

Department of Chemistry and Chemical Biology, Cornell University, Ithaca, New York 14853, United States

Funding

Universit? degli Studi di Milano · National Aeronautics and Space Administration · Fundamental Research Funds for the Central Universities · National Natural Science Foundation of China · Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province

References (85)