Science‐Driven Atomistic Machine Learning
Abstract: 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.
- Standort
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Deutsche Nationalbibliothek Frankfurt am Main
- Umfang
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Online-Ressource
- Sprache
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Englisch
- Erschienen in
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Science‐Driven Atomistic Machine Learning ; day:13 ; month:04 ; year:2023 ; extent:15
Angewandte Chemie ; (13.04.2023) (gesamt 15)
- Urheber
- DOI
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10.1002/ange.202219170
- URN
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urn:nbn:de:101:1-2023041315341635638348
- Rechteinformation
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Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Letzte Aktualisierung
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14.08.2025, 10:50 MESZ
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Deutsche Nationalbibliothek. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
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