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Science-Driven Atomistic Machine Learning
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DOI：10.1002/anie.202219170.png)
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Abstract
En 中文
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.
Keywords:
Artificial Intelligence
Atomistic Simulations
Chemical Data
Machine Learning
Molecular Dynamics
Journal
IF:
16.9
Papers:
5.6W
Citations:
53.0W