University of Birmingham > Talks@bham > Data Science and Computational Statistics Seminar > Machine learning for coarse-graining molecular systems

Machine learning for coarse-graining molecular systems

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A coarse-grained description of atomistic systems in molecular dynamics is provided by reaction coordinates. These nonlinear functions of the atomic positions are a basic ingredient to compute more efficiently average properties of the system of interest, such as free energy profiles. However, reaction coordinates are often based on an intuitive understanding of the system, and one would like to complement this intuition or even replace it with automated tools. One appealing tool is autoencoders, for which the bottleneck layer provides a low dimensional representation of high dimensional atomistic systems. I will discuss some mathematical foundations of this method, and present illustrative applications including alanine dipeptide and chignolin. Some on-going extensions to more demanding systems, namely HSP90 , will also be hinted at. Joint work with Zineb Belkacemi (Sanofi and Ecole des Ponts), Tony Lelièvre (Ecole des Ponts and Inria Paris), Gkeka Paraskevi (Sanofi).

This talk is part of the Data Science and Computational Statistics Seminar series.

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