Machine Learning of Coarse-Grained Molecular Dynamics Force Fields

Wang, Jiang; Olsson, Simon; Wehmeyer, Christoph; Pérez, Adrià; Charron, Nicholas E.; Fabritiis, Gianni De; Noé, Frank; Clementi, Cecilia

doi:10.1021/acscentsci.8b00913

articleACS Central ScienceApr 15, 2019DIAMOND OA

Machine Learning of Coarse-Grained Molecular Dynamics Force Fields

JWJiang Wang SOSimon Olsson CWChristoph Wehmeyer APAdrià Pérez NENicholas E. Charron

Center for Theoretical Biological Physics · Rice University · +3 more institutions

PubMed

Indexed incrossrefdoajpubmed

Abstract

Atomistic or ab initio molecular dynamics simulations are widely used to predict thermodynamics and kinetics and relate them to molecular structure. A common approach to go beyond the time- and length-scales accessible with such computationally expensive simulations is the definition of coarse-grained molecular models. Existing coarse-graining approaches define an effective interaction potential to match defined properties of high-resolution models or experimental data. In this paper, we reformulate coarse-graining as a supervised machine learning problem. We use statistical learning theory to decompose the coarse-graining error and cross-validation to select and compare the performance of different models. We…

Citation impact

519

total citations

FWCI: 23.22
Percentile: 100%
References: 96

Citations per year

Authors

8

Topics & keywords

Topics

Keywords

Granularity
Computer science
Curse of dimensionality
Molecular dynamics
Dimensionality reduction
Artificial intelligence
Statistical physics
Machine learning

UN Sustainable Development Goals

Affordable and clean energy

No related works found for this paper.