sGDML: constructing accurate and data efficient molecular force fields using machine learning

DOI10.1016/j.cpc.2019.02.007OpenAlexW2904141086MaRDI QIDQ2696420

Huziel E. Sauceda, Alexandre Tkatchenko, Klaus-Robert Müller, Stefan Chmiela, Igor Poltavsky

Publication date: 14 April 2023

Published in: Computer Physics Communications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1812.04986

zbMATH Keywords

quantum chemistryab initio molecular dynamicspath integral molecular dynamicscoupled cluster calculationsgradient domain machine learningmachine learning force fieldmachine learning potentialmolecular property prediction

Mathematics Subject Classification ID

Mechanics of deformable solids (74-XX) Quantum theory (81-XX)

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