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

Author:

Ge Fuchun1ORCID,Wang Ran1,Qu Chen2,Zheng Peikun1ORCID,Nandi Apurba34ORCID,Conte Riccardo5ORCID,Houston Paul L.6ORCID,Bowman Joel M.3ORCID,Dral Pavlo O.1ORCID

Affiliation:

1. 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

2. Independent Researcher, Toronto, Ontario M9B0E3, Canada

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

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

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

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

Funder

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

Publisher

American Chemical Society (ACS)

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