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Machine Learning with and for Molecular Dynamics Simulations

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From simple clustering techniques to more sophisticated neural networks, the use of machine learning has become a valuable tool in many fields of chemistry in the past decades. Here, we describe two different ways in which we explore the combination of machine learning (ML) and molecular dynamics (MD) simulations. One topic focuses on how the information in MD simulations can be encoded such that it can be used as input to train ML models for the quantitative understanding of molecular systems. The second topic addresses the utilization of machine learning to improve the set-up, interpretation, as well as accuracy of MD simulations.
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Document Type: Research Article

Affiliations: 1: Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, CH-8092 Zurich;, Email: [email protected] 2: Laboratory of Physical Chemistry, ETH Zurich, Vladimir-Prelog-Weg 2, CH-8092 Zurich

Publication date: December 1, 2019

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  • International Journal for Chemistry and Official Membership Journal of the Swiss Chemical Society (SCS) and its Divisions

    CHIMIA, a scientific journal for chemistry in the broadest sense, is published 10 times a year and covers the interests of a wide and diverse readership. Contributions from all fields of chemistry and related areas are considered for publication in the form of Review Articles and Notes. A characteristic feature of CHIMIA are the thematic issues, each devoted to an area of great current significance.

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