(2) did not consider explicit solvent molecules and was limited to less than 10 ps of simulation. The first molecular dynamics (MD) study of a protein in 1977 by McCammon et al. (1) As such, an atomically resolved picture can provide invaluable insights on biological (and other) processes. In 1964, physicist Richard Feynman famously remarked “that all things are made of atoms, and that everything that living things do can be understood in terms of the jigglings and wigglings of atoms”. The text concludes with a discussion of the challenges that remain to be overcome by the next generation of ML-FFs. 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. This review gives an overview of applications of ML-FFs and the chemical insights that can be obtained from them. Such universal ML approximations are in principle only limited by the quality and quantity of the reference data used to train them. 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. 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. 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.

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