Science‐Driven Atomistic Machine Learning

Abstract

AbstractMachine learning (ML) algorithms are currently emerging as powerful tools in all areas of science. Conventionally, ML is understood as a fundamentally data‐driven endeavour. Unfortunately, large well‐curated databases are sparse in chemistry. In this contribution, I therefore review science‐driven ML approaches which do not rely on “big data”, focusing on the atomistic modelling of materials and molecules. In this context, the term science‐driven refers to approaches that begin with a scientific question and then ask what training data and model design choices are appropriate. As key features of science‐driven ML, the automated and purpose‐driven collection of data and the use of chemical and physical priors to achieve high data‐efficiency are discussed. Furthermore, the importance of appropriate model evaluation and error estimation is emphasized.