Machine Learning Potential Energy Surfaces

Oliver T. Unke; Markus Meuwly

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arXiv · 1909.08027

Machine Learning Potential Energy Surfaces

Oliver T. Unke·Markus Meuwly

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Abstract

Machine Learning techniques can be used to represent high-dimensional potential energy surfaces for reactive chemical systems. Two such methods are based on a reproducing kernel Hilbert space representation or on deep neural networks. They can achieve a sub-1 kcal/mol accuracy with respect to reference data and can be used in studies of chemical dynamics. Their construction and a few typical examples are briefly summarized in the present contribution.

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Oliver T. Unke, Markus Meuwly. 2019-09-17. Machine Learning Potential Energy Surfaces. https://arxiv.org/abs/1909.08027

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