Science-Driven Atomistic Machine Learning

Machine 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.

Automated summary

Machine learning algorithms are powerful tools in science, but large well-curated databases are sparse in chemistry. This contribution reviews science-driven machine learning approaches that focus on atomistic modelling of materials and molecules. Science-driven machine learning involves starting with a scientific question and determining appropriate training data and model design choices. It emphasizes automated and purpose-driven data collection, the use of chemical and physical priors for efficient data usage, and the importance of appropriate model evaluation and error estimation.