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Atomistic Simulations for Reactions and Vibrational Spectroscopy in the Era of Machine Learning - Quo Vadis?
JournalArticle (Originalarbeit in einer wissenschaftlichen Zeitschrift)
 
ID 4660793
Author(s) Meuwly, Markus
Author(s) at UniBasel Meuwly, Markus
Year 2022
Title Atomistic Simulations for Reactions and Vibrational Spectroscopy in the Era of Machine Learning - Quo Vadis?
Journal Journal of Physical Chemistry B
Volume 126
Number 11
Pages / Article-Number 2155-2167
Abstract Atomistic simulations using accurate energy functions can provide molecular-level insight into functional motions of molecules in the gas and in the condensed phase. This Perspective delineates the present status of the field from the efforts of others and some of our own work and discusses open questions and future prospects. The combination of physics-based long-range representations using multipolar charge distributions and kernel representations for the bonded interactions is shown to provide realistic models for the exploration of the infrared spectroscopy of molecules in solution. For reactions, empirical models connecting dedicated energy functions for the reactant and product states allow statistically meaningful sampling of conformational space whereas machine-learned energy functions are superior in accuracy. The future combination of physics-based models with machine-learning techniques and integration into all-purpose molecular simulation software provides a unique opportunity to bring such dynamics simulations closer to reality.
Publisher American Chemical Society
ISSN/ISBN 1520-6106 ; 1520-5207
edoc-URL https://edoc.unibas.ch/93163/
Full Text on edoc No
Digital Object Identifier DOI 10.1021/acs.jpcb.2c00212
PubMed ID http://www.ncbi.nlm.nih.gov/pubmed/35286087
ISI-Number WOS:000778011400002
Document type (ISI) Article
 
   

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