Issue 3, 2023

Machine learning in computational chemistry: interplay between (non)linearity, basis sets, and dimensionality

Abstract

Machine learning (ML) based methods and tools have now firmly established themselves in physical chemistry and in particular in theoretical and computational chemistry and in materials chemistry. The generality of popular ML techniques such as neural networks or kernel methods (Gaussian process and kernel ridge regression and their flavors) permitted their application to diverse problems from prediction of properties of functional materials (catalysts, solid state ionic conductors, etc.) from descriptors to the building of interatomic potentials (where ML is currently routinely used in applications) and electron density functionals. These ML techniques are assumed to have superior expressive power of nonlinear methods, and are often used “as is”, with concepts such as “non-parametric” or “deep learning“ used without a clear justification for their need or advantage over simpler and more robust alternatives. In this Perspective, we highlight some interrelations between popular ML techniques and traditional linear regressions and basis expansions and demonstrate that in certain regimes (such as a very high dimensionality) these approximations might collapse. We also discuss ways to recover the expressive power of a nonlinear approach and to help select hyperparameters with the help of high-dimensional model representation and to obtain elements of insight while preserving the generality of the method.

Graphical abstract: Machine learning in computational chemistry: interplay between (non)linearity, basis sets, and dimensionality

Article information

Article type
Perspective
Submitted
07 sep 2022
Accepted
06 dec 2022
First published
07 dec 2022

Phys. Chem. Chem. Phys., 2023,25, 1546-1555

Machine learning in computational chemistry: interplay between (non)linearity, basis sets, and dimensionality

S. Manzhos, S. Tsuda and M. Ihara, Phys. Chem. Chem. Phys., 2023, 25, 1546 DOI: 10.1039/D2CP04155C

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