Optimized multifidelity machine learning for quantum chemistry

Optimized multifidelity machine learning for quantum chemistry

Machine learning (ML) provides access to fast and accurate quantum chemistry (QC) calculations for various properties of interest such as excitation energies. It is often the case that high accuracy in prediction using a ML model, demands a large and costly training set. Various solutions and proced...

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Bibliografiska uppgifter
Huvudupphovsmän: Vivin Vinod, Ulrich Kleinekathöfer, Peter Zaspel
Materialtyp: Artikel
Språk:English
Publicerad: IOP Publishing 2024-01-01
Serie:Machine Learning: Science and Technology
Ämnen:
machine learning
multifidelity machine learning
kernel ridge regression
electronic structure theory
basis sets
electron correlation
Länkar:https://doi.org/10.1088/2632-2153/ad2cef

Internet

https://doi.org/10.1088/2632-2153/ad2cef

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