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… 
Coarse-graining auto-encoders for molecular dynamics
TLDR
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.
Towards exact molecular dynamics simulations with invariant machine-learned models
TLDR
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.
Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins
TLDR
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.
Differentiable molecular simulation can learn all the parameters in a coarse-grained force field for proteins
TLDR
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.
Coarse-graining molecular systems by spectral matching.
TLDR
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.
Theory and Practice of Coarse-Grained Molecular Dynamics of Biologically Important Systems
TLDR
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.
Kernel-Based Machine Learning for Efficient Simulations of Molecular Liquids
TLDR
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.
Deep learning for variational multiscale molecular modeling.
TLDR
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
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
Backmapping coarse-grained macromolecules: An efficient and versatile machine learning approach.
TLDR
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.
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 107 REFERENCES
Neural Network Based Prediction of Conformational Free Energies - A New Route toward Coarse-Grained Simulation Models.
TLDR
A new method where a neural network is used to extract high-dimensional free energy surfaces (FES) from molecular dynamics simulation trajectories and it is shown that the NN not only is able to correctly describe the free-energy surface for oligomer lengths that it was trained on but also was able to predict the conformational sampling of longer chains.
Towards exact molecular dynamics simulations with machine-learned force fields
TLDR
A flexible machine-learning force-field with high-level accuracy for molecular dynamics simulations is developed, for flexible molecules with up to a few dozen atoms and insights into the dynamical behavior of these molecules are provided.
Machine learning coarse grained models for water
TLDR
A machine-learned coarse-grained water model to elucidate the ice nucleation process much more efficiently than previous models is developed, in a significant departure from conventional force-field fitting.
Learning Effective Molecular Models from Experimental Observables.
TLDR
It is shown that when the correct coarsening resolution is used not only do the optimized models match the Reference model simulated experimental data accurately but additional observables not directly targeted during the optimization procedure are also reproduced.
Coarse-graining entropy, forces, and structures.
TLDR
The present work investigates parallels between these seemingly divergent approaches by examining the relative entropy and multiscale coarse-graining (MS-CG) methods, and demonstrates that the MS-CG method minimizes the average of its gradient squared.
Machine-Learned Coarse-Grained Models.
TLDR
This work presents a novel FF optimization framework that integrates MD simulations with particle swarm optimization (PSO) algorithm and artificial neural network (ANN), which was used to develop transferable coarse-grained models for D2O and DMF as a proof of concept.
Machine learning of accurate energy-conserving molecular force fields
TLDR
The GDML approach enables quantitative molecular dynamics simulations for molecules at a fraction of cost of explicit AIMD calculations, thereby allowing the construction of efficient force fields with the accuracy and transferability of high-level ab initio methods.
Many-Body Coarse-Grained Interactions Using Gaussian Approximation Potentials.
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
DeePCG: Constructing coarse-grained models via deep neural networks.
TLDR
It is found that the two-body, three- body, and higher-order oxygen correlation functions produced by the coarse-grained and full atomistic models agree very well with each other, illustrating the effectiveness of the DeePCG model on a rather challenging task.
The multiscale coarse-graining method. I. A rigorous bridge between atomistic and coarse-grained models.
TLDR
The present work develops a formal statistical mechanical framework for the MS-CG method and demonstrates that the variational principle underlying the method may, in principle, be employed to determine the many-body potential of mean force (PMF) that governs the equilibrium distribution of positions of the CG sites for theMS-CG models.
...
1
2
3
4
5
...