Issue 1, 2023

Neural network potentials for chemistry: concepts, applications and prospects

Abstract

Artificial Neural Networks (NN) are already heavily involved in methods and applications for frequent tasks in the field of computational chemistry such as representation of potential energy surfaces (PES) and spectroscopic predictions. This perspective provides an overview of the foundations of neural network-based full-dimensional potential energy surfaces, their architectures, underlying concepts, their representation and applications to chemical systems. Methods for data generation and training procedures for PES construction are discussed and means for error assessment and refinement through transfer learning are presented. A selection of recent results illustrates the latest improvements regarding accuracy of PES representations and system size limitations in dynamics simulations, but also NN application enabling direct prediction of physical results without dynamics simulations. The aim is to provide an overview for the current state-of-the-art NN approaches in computational chemistry and also to point out the current challenges in enhancing reliability and applicability of NN methods on a larger scale.

Graphical abstract: Neural network potentials for chemistry: concepts, applications and prospects

Article information

Article type
Perspective
Submitted
23 sen 2022
Accepted
20 dek 2022
First published
21 dek 2022
This article is Open Access
Creative Commons BY license

Digital Discovery, 2023,2, 28-58

Neural network potentials for chemistry: concepts, applications and prospects

S. Käser, L. I. Vazquez-Salazar, M. Meuwly and K. Töpfer, Digital Discovery, 2023, 2, 28 DOI: 10.1039/D2DD00102K

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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