Design, assessment, and application of machine learning potential energy surfaces

Abstract Potential energy surfaces (PES) are an indispensable tool in the investigation, characterization and understanding of chemical and biological systems in the gas and condensed phases. Advances in machine learning (ML) methodologies have led to the development of ML-PES, which are now widely used to simulate such systems. This work provides an overview of concepts, methodologies and recommendations for constructing and using ML-PESs. The choice of topics is focused on the practical issues that are commonly found. Application of the principles discussed are illustrated through two different systems of biomolecular importance: the non-reactive dynamics of the Alanine-Lysine-Alanine tripeptide in gas and solution phases, and double proton transfer reactions in DNA base pairs.