Numerical Accuracy Matters: Applications of Machine Learned Potential Energy Surfaces
Author:
Affiliation:
1. Department of Chemistry, University of Basel, Klingelbergstrasse 80, CH-4056 Basel, Switzerland
Funder
Schweizerischer Nationalfonds zur F?rderung der Wissenschaftlichen Forschung
Universit?t Basel
U.S. Air Force
Publisher
American Chemical Society (ACS)
Link
https://pubs.acs.org/doi/pdf/10.1021/acs.jpclett.3c03405
Reference51 articles.
1. Neural network models of potential energy surfaces
2. Combiningabinitiocomputations, neural networks, and diffusion Monte Carlo: An efficient method to treat weakly bound molecules
3. Neural Network Potential Energy Surfaces for Small Molecules and Reactions
4. High-Fidelity Potential Energy Surfaces for Gas-Phase and Gas–Surface Scattering Processes from Machine Learning
5. Machine Learning Force Fields
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