Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials.

By ST John, Gábor Csányi. 2016

126 citations recorded by the source corpus.

We introduce a computational framework that is able to describe general many-body coarse-grained (CG) interactions of molecules and use it to model the free energy surface of molecular liquids as a cluster expansion in terms of monomer, dimer, and trimer terms. The contributions to the free energy due to these terms are inferred from all-atom molecular dynamics (MD) data using Gaussian Approximation Potentials, a type of machine-learning model that employs Gaussian process regression. The resulting CG model is much more accurate than those possible using pair potentials. Though slower than the latter, our model can still be faster than all-atom simulations for solvent-free CG models commonly used in biomolecular simulations.

Subfields: Computational Theory and Mathematics, Materials Chemistry, Molecular Biology

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Citations in the corpus

Machine learning for molecular simulation (2019, methodology): “In addition to atomistic force fields, it has been recently shown that, in the same spirit, effective molecular models at resolution coarser than atomistic can be designed by ML [16, 17, 18].”
Coarse graining molecular dynamics with graph neural networks. (2020, methodology): “Modern machine learning techniques have been recently employed to learn both the mapping 15,16 and the model 17–22 components of bottom up coarse graining.”