Enhancing the Quality and Reliability of Machine Learning Interatomic Potentials through Better Reporting Practices-Reference-Cited by-同舟云学术

Enhancing the Quality and Reliability of Machine Learning Interatomic Potentials through Better Reporting Practices

Published:2024-03-20 Issue:16 Volume:128 Page:6524-6537
ISSN:1932-7447
Container-title:The Journal of Physical Chemistry C
language:en
Short-container-title:J. Phys. Chem. C

Author:

Maxson Tristan¹^ORCID,Soyemi Ademola¹,Chen Benjamin W. J.²^ORCID,Szilvási Tibor¹^ORCID

Affiliation:

1. Department of Chemical and Biological Engineering, University of Alabama, Tuscaloosa, Alabama 35487, United States

2. Institute of High Performance Computing (IHPC), Agency for Science, Technology, and Research (A*STAR), 1 Fusionopolis Way, #16−16 Connexis, Singapore 138632, Singapore

Funder

Advanced Scientific Computing Research

Chemical Sciences, Geosciences, and Biosciences Division

Division of Chemical, Bioengineering, Environmental, and Transport Systems

University of Alabama

Science and Engineering Research Council

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.jpcc.4c00028

Reference142 articles.

1. Fluorine spillover for ceria- vs silica-supported palladium nanoparticles: A MD study using machine learning potentials

2. Dynamical Study of Adsorbate-Induced Restructuring Kinetics in Bimetallic Catalysts Using the PdAu(111) Model System

3. Machine learning for protein folding and dynamics

4. Non-covalent interactions across organic and biological subsets of chemical space: Physics-based potentials parametrized from machine learning

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