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Science-Driven Atomistic Machine Learning

Title data

Margraf, Johannes T.:
Science-Driven Atomistic Machine Learning.
In: Angewandte Chemie International Edition. Vol. 62 (2023) Issue 26 . - e202219170.
ISSN 1521-3773
DOI: https://doi.org/10.1002/anie.202219170

Abstract in another language

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.

Further data

Item Type: Article in a journal
Refereed: Yes
Institutions of the University: Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Künstliche Intelligenz in der physiko-chemischen Materialanalytik
Faculties > Faculty of Biology, Chemistry and Earth Sciences > Department of Chemistry > Chair Künstliche Intelligenz in der physiko-chemischen Materialanalytik > Chair Künstliche Intelligenz in der physiko-chemischen Materialanalytik - Univ.-Prof. Dr. Johannes Theo Margraf
Result of work at the UBT: No
DDC Subjects: 500 Science > 540 Chemistry
Date Deposited: 13 Nov 2023 12:18
Last Modified: 13 Nov 2023 12:18
URI: https://eref.uni-bayreuth.de/id/eprint/87665