Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approach

• URL: http://arxiv.org/abs/2005.01851v1
• Date: Mon, 4 May 2020 21:20:01 GMT
• Title: Ensemble Learning of Coarse-Grained Molecular Dynamics Force Fields with a Kernel Approach
• Authors: Jiang Wang, Stefan Chmiela, Klaus-Robert M\"uller, Frank No\`e, Cecilia Clementi
• Abstract summary: Gradient-domain machine learning (GDML) is an accurate and efficient approach to learn a molecular potential and associated force field. We demonstrate its application to learn an effective coarse-grained (CG) model from all-atom simulation data. Using ensemble learning and stratified sampling, we propose a data-efficient and memory-saving alternative.
• Score: 2.562811344441631
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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 coarse-grained force field is learned by following the thermodynamic consistency principle, here by minimizing the error between the predicted coarse-grained force and the all-atom mean force in the coarse-grained 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 coarse-grained model. We illustrate our method on a simple biomolecular system, alanine dipeptide, by reconstructing the free energy landscape of a coarse-grained 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.

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