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Journal Article

Science-Driven Atomistic Machine Learning


Margraf,  Johannes
Theory, Fritz Haber Institute, Max Planck Society;

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Margraf, J. (2023). Science-Driven Atomistic Machine Learning. Angewandte Chemie International Edition, e202219170. doi:10.1002/anie.202219170.

Cite as: https://hdl.handle.net/21.11116/0000-000C-C5BA-4
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.