A Status Report on “Gold Standard” Machine-Learned Potentials for Water

Author:

Yu Qi1ORCID,Qu Chen2ORCID,Houston Paul L.34ORCID,Nandi Apurba15ORCID,Pandey Priyanka1,Conte Riccardo6ORCID,Bowman Joel M.1ORCID

Affiliation:

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

2. Independent Researcher, Toronto, Ontario M9B 0E3, Canada

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

4. Department of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States

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

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

Funder

Universit? degli Studi di Milano

National Aeronautics and Space Administration

Publisher

American Chemical Society (ACS)

Subject

General Materials Science,Physical and Theoretical Chemistry

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1. Simple and Accurate One-Body Energy and Dipole Moment Surfaces for Water and Beyond;The Journal of Physical Chemistry Letters;2024-06-20

2. The Near-Sightedness of Many-Body Interactions in Anharmonic Vibrational Couplings;Journal of the American Chemical Society;2024-05-21

3. Perspective: Atomistic simulations of water and aqueous systems with machine learning potentials;The Journal of Chemical Physics;2024-05-01

4. Transferable Water Potentials Using Equivariant Neural Networks;The Journal of Physical Chemistry Letters;2024-03-28

5. On the brink of self-hydration: the water heptadecamer;Physical Chemistry Chemical Physics;2024

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