Ensemble learning of coarse-grained molecular dynamics force fields with a kernel approach.

By Jiang Wang, Stefan Chmiela, K. Müller, Frank No'e, Cecilia Clementi. 2020

49 citations recorded by the source corpus.

Gradient-domain machine learning (GDML) is an accurate and efficient approach to learn a molecular potential and associated force field based on the kernel ridge regression algorithm. Here, we demonstrate its application to learn an effective coarse-grained (CG) model from all-atom simulation data in a sample efficient manner. The CG force field is learned by following the thermodynamic consistency principle, here by minimizing the error between the predicted CG force and the all-atom mean force in the CG coordinates. Solving this problem by GDML directly is impossible because coarse-graining requires averaging over many training data points, resulting in impractical memory requirements for storing the kernel matrices. In this work, we propose a data-efficient and memory-saving alternative. Using ensemble learning and stratified sampling, we propose a 2-layer training scheme that enables GDML to learn an effective CG model. We illustrate our method on a simple biomolecular system, alanine dipeptide, by reconstructing the free energy landscape of a CG variant of this molecule. Our novel GDML training scheme yields a smaller free energy error than neural networks when the training set is small, and a comparably high accuracy when the training set is sufficiently large.

Subfields: Electrical and Electronic Engineering, Materials Chemistry, Molecular Biology

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Citations in the corpus

Relevance of Rotationally Equivariant Convolutions for Predicting Molecular Properties (2020, background): “…properties, which depend only on the atomic distance graph, are commonly predicted by kernel methods or Gaussian process regression [14, 15, 16, 17] or graph neural networks [18, 19], where ENNs are usually employed for predicting physical properties, which depend on atomic displacement…”
Coarse graining molecular dynamics with graph neural networks. (2020, methodology): “In a nonregularized CGnet, a neural network directly regresses the true forces upon the input data by learning an energy term and then evaluating its derivative.”