scholarly journals Preface: Special Topic on Enhanced Sampling for Molecular Systems

2018 ◽  
Vol 149 (7) ◽  
pp. 072001 ◽  
Author(s):  
Alessandro Laio ◽  
Athanassios Z. Panagiotopoulos ◽  
Daniel M. Zuckerman
Keyword(s):  
Special Topic ◽  
Enhanced Sampling ◽  
Molecular Systems

scholarly journals Deep Learning for Variational Multi-Scale Molecular Modeling

2020 ◽  
Author(s):  
Jun Zhang ◽  
Yaokun Lei ◽  
Yi Isaac Yang ◽  
Yi Qin Gao
Keyword(s):  
Coarse Grained ◽  
Atomistic Simulations ◽  
Molecular Models ◽  
Enhanced Sampling ◽  
Physical Systems ◽  
Molecular Systems ◽  
Fine Grained ◽  
Multi Scale ◽  
Machine Learning Approach ◽  
Training Objective

Molecular simulations are widely applied in the study of chemical and bio-physical systems. However, the<br>accessible timescales of atomistic simulations are limited, and extracting equilibrium properties of systems<br>containing rare events remains challenging. Two distinct strategies are usually adopted in this regard: either<br>sticking to the atomistic level and performing enhanced sampling, or trading details for speed by leveraging<br>coarse-grained models. Although both strategies are promising, either of them, if adopted individually,<br>exhibits severe limitations. In this paper we propose a machine-learning approach to ally both strategies so<br>that simulations on different scales can benefit mutually from their cross-talks: Accurate coarse-grained (CG)<br>models can be inferred from the fine-grained (FG) simulations through deep generative learning; In turn, FG<br>simulations can be boosted by the guidance of CG models via deep reinforcement learning. Our method<br>defines a variational and adaptive training objective which allows end-to-end training of parametric<br>molecular models using deep neural networks. Through multiple experiments, we show that our method is<br>efficient and flexible, and performs well on challenging chemical and bio-molecular systems. <br>

scholarly journals Deep Learning for Variational Multi-Scale Molecular Modeling

2020 ◽  
Author(s):  
Jun Zhang ◽  
Yaokun Lei ◽  
Yi Isaac Yang ◽  
Yi Qin Gao
Keyword(s):  
Coarse Grained ◽  
Atomistic Simulations ◽  
Molecular Models ◽  
Enhanced Sampling ◽  
Physical Systems ◽  
Molecular Systems ◽  
Fine Grained ◽  
Multi Scale ◽  
Machine Learning Approach ◽  
Training Objective

Molecular simulations are widely applied in the study of chemical and bio-physical systems. However, the<br>accessible timescales of atomistic simulations are limited, and extracting equilibrium properties of systems<br>containing rare events remains challenging. Two distinct strategies are usually adopted in this regard: either<br>sticking to the atomistic level and performing enhanced sampling, or trading details for speed by leveraging<br>coarse-grained models. Although both strategies are promising, either of them, if adopted individually,<br>exhibits severe limitations. In this paper we propose a machine-learning approach to ally both strategies so<br>that simulations on different scales can benefit mutually from their cross-talks: Accurate coarse-grained (CG)<br>models can be inferred from the fine-grained (FG) simulations through deep generative learning; In turn, FG<br>simulations can be boosted by the guidance of CG models via deep reinforcement learning. Our method<br>defines a variational and adaptive training objective which allows end-to-end training of parametric<br>molecular models using deep neural networks. Through multiple experiments, we show that our method is<br>efficient and flexible, and performs well on challenging chemical and bio-molecular systems. <br>

scholarly journals Deep Learning for Multi-Scale Molecular Modeling

2020 ◽  
Author(s):  
Jun Zhang ◽  
Yaokun Lei ◽  
Yi Isaac Yang ◽  
Yi Qin Gao
Keyword(s):  
Coarse Grained ◽  
Atomistic Simulations ◽  
Molecular Models ◽  
Enhanced Sampling ◽  
Physical Systems ◽  
Molecular Systems ◽  
Fine Grained ◽  
Multi Scale ◽  
Machine Learning Approach ◽  
Training Objective

Molecular simulations are widely applied in the study of chemical and bio-physical systems. However, the<br>accessible timescales of atomistic simulations are limited, and extracting equilibrium properties of systems<br>containing rare events remains challenging. Two distinct strategies are usually adopted in this regard: either<br>sticking to the atomistic level and performing enhanced sampling, or trading details for speed by leveraging<br>coarse-grained models. Although both strategies are promising, either of them, if adopted individually,<br>exhibits severe limitations. In this paper we propose a machine-learning approach to ally both strategies so<br>that simulations on different scales can benefit mutually from their cross-talks: Accurate coarse-grained (CG)<br>models can be inferred from the fine-grained (FG) simulations through deep generative learning; In turn, FG<br>simulations can be boosted by the guidance of CG models via deep reinforcement learning. Our method<br>defines a variational and adaptive training objective which allows end-to-end training of parametric<br>molecular models using deep neural networks. Through multiple experiments, we show that our method is<br>efficient and flexible, and performs well on challenging chemical and bio-molecular systems. <br>

Reinforcement Learning for Multi-Scale Molecular Modeling

2019 ◽  
Author(s):  
Jun Zhang ◽  
Yaokun Lei ◽  
Yi Isaac Yang
Keyword(s):  
Reinforcement Learning ◽  
Parametric Models ◽  
Coarse Grained ◽  
Enhanced Sampling ◽  
Molecular Systems ◽  
Fine Grained ◽  
Multi Scale ◽  
Machine Learning Approach ◽  
Atom Level ◽  
Training Objective

Molecular simulations are widely applied in the study of chemical and bio-physical systems of interest. However, the accessible timescales of atomistic simulations are limited, and extracting equilibrium properties of systems containing rare events remains challenging. Two distinct strategies are usually adopted in this regard: either sticking to the atom level and performing enhanced sampling, or trading details for speed by leveraging coarse-grained models. Although both strategies are promising, either of them, if adopted individually, exhibits severe limitations. In this paper we propose a machine-learning approach to ally these two worlds. In our approach, simulations on different scales are executed simultaneously and benefit mutually from their cross-talks: Accurate coarse-grained (CG) models can be inferred from the fine-grained (FG) simulations; In turn, FG simulations can be boosted by the guidance of CG models. Our method grounds on unsupervised and reinforcement learning, defined by a variational and adaptive training objective, and allows end-to-end and online training of parametric models. Through multiple experiments, we show that our method is efficient and flexible, and performs well on challenging chemical and bio-molecular systems.

scholarly journals Reinforced dynamics for enhanced sampling in large atomic and molecular systems

2018 ◽  
Vol 148 (12) ◽  
pp. 124113 ◽  
Author(s):  
Linfeng Zhang ◽  
Han Wang ◽  
Weinan E
Keyword(s):  
Enhanced Sampling ◽  
Molecular Systems

scholarly journals Local and global perspectives on diffusion maps in the analysis of molecular systems

2020 ◽  
Vol 476 (2233) ◽  
pp. 20190036 ◽  
Author(s):  
Z. Trstanova ◽  
B. Leimkuhler ◽  
T. Lelièvre
Keyword(s):  
Local Equilibrium ◽  
High Dimensional ◽  
Diffusion Maps ◽  
Enhanced Sampling ◽  
Simulation Data ◽  
Molecular Systems ◽  
Global Perspectives ◽  
Dynamics Problems ◽  
Alanine Dipeptide ◽  
Quasi Stationary Distribution

Diffusion maps approximate the generator of Langevin dynamics from simulation data. They afford a means of identifying the slowly evolving principal modes of high-dimensional molecular systems. When combined with a biasing mechanism, diffusion maps can accelerate the sampling of the stationary Boltzmann–Gibbs distribution. In this work, we contrast the local and global perspectives on diffusion maps, based on whether or not the data distribution has been fully explored. In the global setting, we use diffusion maps to identify metastable sets and to approximate the corresponding committor functions of transitions between them. We also discuss the use of diffusion maps within the metastable sets, formalizing the locality via the concept of the quasi-stationary distribution and justifying the convergence of diffusion maps within a local equilibrium. This perspective allows us to propose an enhanced sampling algorithm. We demonstrate the practical relevance of these approaches both for simple models and for molecular dynamics problems (alanine dipeptide and deca-alanine).

scholarly journals Reinforcement Learning for Multi-Scale Molecular Modeling

2020 ◽  
Author(s):  
Jun Zhang ◽  
Yaokun Lei ◽  
Yi Isaac Yang ◽  
Yi Qin Gao
Keyword(s):  
Reinforcement Learning ◽  
Parametric Models ◽  
Coarse Grained ◽  
Atomistic Simulations ◽  
Enhanced Sampling ◽  
Molecular Systems ◽  
Fine Grained ◽  
Multi Scale ◽  
Machine Learning Approach ◽  
Training Objective

Molecular simulations are widely applied in the study of chemical and bio-physical systems of interest. However, the accessible timescales of atomistic simulations are limited, and extracting equilibrium properties of systems containing rare events remains challenging. Two distinct strategies are usually adopted in this regard: either sticking to the atomistic level and performing enhanced sampling, or trading details for speed by leveraging coarse-grained models. Although both strategies are promising, either of them, if adopted individually, exhibits severe limitations. In this paper we propose a machine-learning approach to take advantage of both strategies. In this approach, simulations on different scales are executed simultaneously and benefit mutually from their cross-talks: Accurate coarse-grained (CG) models can be inferred from the fine-grained (FG) simulations; In turn, FG simulations can be boosted by the guidance of CG models. Our method grounds on unsupervised and reinforcement learning, defined by a variational and adaptive training objective, and allows end-to-end training of parametric models. Through multiple experiments, we show that our method is efficient and flexible, and performs well on challenging chemical and bio-molecular systems.

scholarly journals Spectral gap optimization of order parameters for sampling complex molecular systems

2016 ◽  
Vol 113 (11) ◽  
pp. 2839-2844 ◽  
Author(s):  
Pratyush Tiwary ◽  
B. J. Berne
Keyword(s):  
Spectral Gap ◽  
Umbrella Sampling ◽  
Order Parameters ◽  
Enhanced Sampling ◽  
Many Body ◽  
Collective Variables ◽  
Molecular Systems ◽  
Many Body Systems ◽  
Entropy Estimate ◽  
Low Dimensional

In modern-day simulations of many-body systems, much of the computational complexity is shifted to the identification of slowly changing molecular order parameters called collective variables (CVs) or reaction coordinates. A vast array of enhanced-sampling methods are based on the identification and biasing of these low-dimensional order parameters, whose fluctuations are important in driving rare events of interest. Here, we describe a new algorithm for finding optimal low-dimensional CVs for use in enhanced-sampling biasing methods like umbrella sampling, metadynamics, and related methods, when limited prior static and dynamic information is known about the system, and a much larger set of candidate CVs is specified. The algorithm involves estimating the best combination of these candidate CVs, as quantified by a maximum path entropy estimate of the spectral gap for dynamics viewed as a function of that CV. The algorithm is called spectral gap optimization of order parameters (SGOOP). Through multiple practical examples, we show how this postprocessing procedure can lead to optimization of CV and several orders of magnitude improvement in the convergence of the free energy calculated through metadynamics, essentially giving the ability to extract useful information even from unsuccessful metadynamics runs.

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