Machine Learning with and for Molecular Dynamics Simulations

被引：6

作者：

Riniker, Sereina ^{[1
]}

Wang, Shuzhe ^{[1
]}

Bleiziffer, Patrick ^{[1
]}

Boeselt, Lennard ^{[1
]}

Esposito, Carmen ^{[1
]}

机构：

[1] Swiss Fed Inst Technol, Lab Phys Chem, Vladimir Prelog Weg 2, CH-8092 Zurich, Switzerland

来源：

CHIMIA | 2019年 / 73卷 / 12期

基金：

瑞士国家科学基金会;

关键词：

Machine learning; Molecular dynamics; MARKOV STATE MODELS; WATER DISTRIBUTION COEFFICIENTS; AQUEOUS SOLUBILITY; FORCE-FIELD; PREDICTION; CHEMOINFORMATICS; ZINC;

D O I：

10.2533/chimia.2019.1024

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

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.

引用

页码：1024 / 1027

页数：4

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