A New Paradigm for Computational Chemistry

TL;DR

This paper discusses a paradigm shift in computational chemistry driven by foundation machine learning interatomic potentials, which promise quantum accuracy at force-field speeds, potentially replacing DFT.

Contribution

It introduces foundation machine learning potentials that overcome previous data requirements, enabling efficient and accurate simulations in computational chemistry.

Findings

Foundation ML potentials achieve quantum accuracy with force-field speed.

Recent developments allow training on large, system-specific datasets.

These potentials are set to replace DFT in many applications within a decade.

Abstract

Computational chemistry has become an indispensable tool for generating data and insights, pervading all branches of experimental chemistry. Its most central concept is the potential energy hypersurface, key to all chemistry and materials science, as it assigns an energy to a molecular structure, the necessary ingredient for reaction mechanism elucidation and reaction rate calculation. Density functional theory (DFT) has been the most important method in practice for obtaining such energies, which is mirrored in the use of high-performance computing hardware. In the last two decades, a new class of surrogate potential energy functions has been evolving with remarkable properties: quantum accuracy combined with force-field speed. Until very recently, their application was hampered by the fact that they needed to be trained on truly large system-specific data sets, generated before a computational chemistry study could be started (in sharp contrast to DFT, which, as a first-principles method, works out of the box, but at a far higher price of computational cost). Very recently, this roadblock has been overcome by so-called foundation machine learning interatomic potentials, which are poised to completely change the way we do computational chemistry, likely prompting us to abandon DFT as the prime method of choice for this purpose in less than a decade.