Machine learning of coarse-grained molecular dynamics force fields

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

doi:http://dx.doi.org/10.1021/acscentsci.8b00913

Machine learning of coarse-grained molecular dynamics force fields

Citation

Wang J, Olsson S, Wehmeyer C, Pérez A, Charron NE, de Fabritiis G et al. Machine learning of coarse-grained molecular dynamics force fields. ACS Cent Sci. 2019;5(5):755-67. DOI: 10.1021/acscentsci.8b00913

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 introduce CGnets, a deep learning approach, that learns coarse-grained free energy functions and can be trained by a force-matching scheme. CGnets maintain all physically relevant invariances and allow one to incorporate prior physics knowledge to avoid sampling of unphysical structures. We show that CGnets can capture all-atom explicit-solvent free energy surfaces with models using only a few coarse-grained beads and no solvent, while classical coarse-graining methods fail to capture crucial features of the free energy surface. Thus, CGnets are able to capture multibody terms that emerge from the dimensionality reduction.