# Machine Learning of Coarse-Grained Molecular Dynamics Force Fields

@article{Wang2018MachineLO, 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={2018}, 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…

## 256 Citations

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This work proposes a 2-layer training scheme that enables GDML to learn an effective coarse-grained (CG) model from all-atom simulation data in a sample efficient manner and yields a smaller free energy error than neural networks when the training set is small, and a comparably high accuracy when theTraining set is sufficiently large.

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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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- BiologyThe journal of physical chemistry letters
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An optimization statement for force mappings is defined and it is demonstrated that substantially improved CG force fields can be learned from the same simulation data when using optimized force maps.

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This work explores the application of SchNet models to obtain a CG potential for liquid benzene, investigating the effect of model architecture and hyperparameters on the thermodynamic, dynamical, and structural properties of the simulated CG systems, reporting and discussing challenges encountered and future directions envisioned.

### Simulate Time-integrated Coarse-grained Molecular Dynamics with Geometric Machine Learning

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A novel score-based GNN refinement module resolves the long-standing challenge of long-time simulation instability and can generalize to unseen novel systems and simulate for much longer than the training trajectories.

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- Computer ScienceThe Journal of chemical physics
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A hybrid architecture for the machine learning of coarse-grained force fields that learn their own features via a subnetwork that leverages continuous filter convolutions on a graph neural network architecture is introduced that succeeds at reproducing the thermodynamics for small biomolecular systems.

### Machine Learning Coarse-Grained Potentials of Protein Thermodynamics

- Computer ScienceArXiv
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It is shown that a single coarse-grained potential can integrate all twelve proteins and can capture experimental structural features of mutated proteins, indicating that machine learning coarse- grained potentials could provide a feasible approach to simulate and understand protein dynamics.

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We develop a machine-learning method for coarse-graining condensed-phase molecular systems using anisotropic particles. The method extends currently available high-dimensional neural network…

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- Computer ScienceThe Journal of chemical physics
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This work uses a graph machine learning framework to embed atomic configurations into a low-dimensional space to produce efficient representations of the original molecular system and shows that this technique is robust, recovering the first two moments of the distribution of several observables in proteins such as chignolin and alanine dipeptide.

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The symmetric GDML (sGDML) approach is able to faithfully reproduce global force fields at the accuracy high-level ab initio methods, thus enabling sample intensive tasks like molecular dynamics simulations at that level of accuracy.

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