# Machine Learning of Coarse-Grained Molecular Dynamics Force Fields

@article{Wang2019MachineLO, title={Machine Learning of Coarse-Grained Molecular Dynamics Force Fields}, author={Jiang Wang and Christoph Wehmeyer and Frank No{\'e} and Cecilia Clementi}, journal={ACS Central Science}, year={2019}, volume={5}, pages={755 - 767} }

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…

## 159 Citations

Coarse-graining auto-encoders for molecular dynamics

- Computer Science, Chemistrynpj Computational Materials
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This work proposes a generative modeling framework based on variational auto-encoders to unify the tasks of learning discrete coarse-grained variables, decoding back to atomistic detail, and parameterizing coarse- grained force fields.

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- Computer Science
- 2019

This thesis develops a combined machine learning (ML) and quantum mechanics approach that enables the direct reconstruction of flexible molecular force fields from high-level ab initio calculations and provides the key missing ingredient for achieving spectroscopic accuracy in molecular simulations.

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- Biology, Computer Science
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This work parameterises a simple coarse-grained force field for proteins, based on training simulations of up to 2,000 steps learning to keep the native structure stable, and shows ability in protein design and model scoring applications.

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- MedicinePloS one
- 2021

This work parameterising a simple coarse-grained force field for proteins, based on training simulations of up to 2,000 steps learning to keep the native structure stable, takes advantage of the deep learning revolution whilst retaining the interpretability and efficiency of existing force fields.

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- Chemistry, Computer ScienceThe Journal of chemical physics
- 2019

This study focuses on data-driven methods to preserve the rare-event kinetics of the original system and make use of their close connection to the low-lying spectrum of the system's generator, using a general framework, called spectral matching, which directly targets the generator's leading eigenvalue equations when learning parameters for coarse-grained models.

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- MedicineBiomolecules
- 2021

It is demonstrated that the motion of coarse-grained sites is governed by the potential of mean force and the friction and stochastic forces, resulting from integrating out the secondary degrees of freedom.

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- Computer Science, MedicineJournal of chemical theory and computation
- 2020

Here, covariant meshing proves to be an efficient strategy to learn canonically averaged instantaneous forces and it is shown that molecular dynamics simulations with tabulated two- and three-body ML potentials are computationally efficient and recover two-and-three-body distribution functions.

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- Medicine, Computer ScienceThe Journal of chemical physics
- 2020

This paper proposes a machine-learning approach to ally both strategies so that simulations on different scales can benefit mutually from their crosstalks, and defines a variational and adaptive training objective, which allows end-to-end training of parametric molecular models using deep neural networks.

Deep Learning for Multi-Scale Molecular Modeling

- 2020

Molecular simulations are widely applied in the study of chemical and bio-physical systems. However, the accessible timescales of atomistic simulations are limited, and extracting equilibrium…

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- MedicineThe Journal of chemical physics
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An image-based approach for structural backmapping from coarse-grained to atomistic models with cis-1,4 polyisoprene melts as an illustrative example is presented and shows remarkable efficiency and transferability over different molecular weights in the melt based on training sets constructed from oligomeric compounds.

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