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Artificial Intelligence Assists Discovery of Reaction Coordinates and Mechanisms from Molecular Dynamics Simulations

Authors :
Jung, Hendrik
Covino, Roberto
Hummer, Gerhard
Publication Year :
2019
Publisher :
arXiv, 2019.

Abstract

Exascale computing holds great opportunities for molecular dynamics (MD) simulations. However, to take full advantage of the new possibilities, we must learn how to focus computational power on the discovery of complex molecular mechanisms, and how to extract them from enormous amounts of data. Both aspects still rely heavily on human experts, which becomes a serious bottleneck when a large number of parallel simulations have to be orchestrated to take full advantage of the available computing power. Here, we use artificial intelligence (AI) both to guide the sampling and to extract the relevant mechanistic information. We combine advanced sampling schemes with statistical inference, artificial neural networks, and deep learning to discover molecular mechanisms from MD simulations. Our framework adaptively and autonomously initializes simulations and learns the sampled mechanism, and is thus suitable for massively parallel computing architectures. We propose practical solutions to make the neural networks interpretable, as illustrated in applications to molecular systems.<br />Comment: 11 pages, 5 figures, supporting information

Details

Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....b000aeb6471b688cf8e94ea468ff473b
Full Text :
https://doi.org/10.48550/arxiv.1901.04595