Choosing the right molecular machine learning potential

Abstract

Quantum-chemistry simulations based on potential energy surfaces of molecules provide invaluable insight into the physicochemical processes at the atomistic level and yield such important observables as reaction rates and spectra. Machine learning potentials promise to significantly reduce the computational cost and hence enable otherwise unfeasible simulations. However, the surging number of such potentials begs the question of which one to choose or whether we still need to develop yet another one. Here, we address this question by evaluating the performance of popular machine learning potentials in terms of accuracy and computational cost. In addition, we deliver structured information for non-specialists in machine learning to guide them through the maze of acronyms, recognize each potential's main features, and judge what they could expect from each one.

Graphical abstract: Choosing the right molecular machine learning potential

Article information

Article type
Edge Article
Submitted
29 Jun 2021
Accepted
14 Sep 2021
First published
15 Sep 2021
This article is Open Access

All publication charges for this article have been paid for by the Royal Society of Chemistry
Creative Commons BY license

Chem. Sci., 2021, Advance Article

Choosing the right molecular machine learning potential

M. Pinheiro, F. Ge, N. Ferré, P. O. Dral and M. Barbatti, Chem. Sci., 2021, Advance Article , DOI: 10.1039/D1SC03564A

This article is licensed under a Creative Commons Attribution 3.0 Unported Licence. You can use material from this article in other publications without requesting further permissions from the RSC, provided that the correct acknowledgement is given.

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