Machine Learning Force Fields

In recent years, the use of Machine Learning
(ML) in computational chemistry has enabled
numerous advances previously out of reach due
to the computational complexity of traditional
electronic-structure methods. One of the most
promising applications is the construction of
ML-based force fields (FFs), with the aim to
narrow the gap between the accuracy of ab initio methods and the efficiency of classical FFs.
The key idea is to learn the statistical relation
between chemical structure and potential energy
without relying on a preconceived notion of fixed
chemical bonds or knowledge about the relevant
interactions. Such universal ML approximations
are in principle only limited by the quality and
quantity of the reference data used to train them.
This review gives an overview of applications
of ML-FFs and the chemical insights that can
be obtained from them. The core concepts underlying ML-FFs are described in detail and a
step-by-step guide for constructing and testing
them from scratch is given. The text concludes
with a discussion of the challenges that remain
to be overcome by the next generation of MLFFs.

• General Science

• Machine Intelligence