Title: Reinforced dynamics for enhanced sampling in large atomic and molecular systems

Abstract

A new approach for efficiently exploring the configuration space and computing the free energy of large atomic and molecular systems is proposed, motivated by an analogy with reinforcement learning. There are two major components in this new approach. Like metadynamics, it allows for an efficient exploration of the configuration space by adding an adaptively computed biasing potential to the original dynamics. Like deep reinforcement learning, this biasing potential is trained on the fly using deep neural networks, with data collected judiciously from the exploration and an uncertainty indicator from the neural network model playing the role of the reward function. Parameterization using neural networks makes it feasible to handle cases with a large set of collective variables. This has the potential advantage that selecting precisely the right set of collective variables has now become less critical for capturing the structural transformations of the system. Finally, the method is illustrated by studying the full-atom explicit solvent models of alanine dipeptide and tripeptide, as well as the system of a polyalanine-10 molecule with 20 collective variables.

Authors:

Zhang, Linfeng  ^[1];

• Search DOE PAGES for author "Zhang, Linfeng"
• Search DOE PAGES for ORCID "0000-0002-8470-5846"

Wang, Han  ^[2];

• Search DOE PAGES for author "Wang, Han"
• Search DOE PAGES for ORCID "0000-0001-5623-1148"

E, Weinan ^[3]

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+ Show Author Affiliations

1. Princeton Univ., Princeton, NJ (United States). Program in Applied and Computational Mathematics
2. Inst. of Applied Physics and Computational Mathematics, Beijing (China); CAEP Software Center for High Performance Numerical Simulation, Beijing (China)
3. Princeton Univ., Princeton, NJ (United States). Dept. of Mathematics and Program in Applied and Computational Mathematics; Beijing Inst. of Big Data Research, Beijing (China)

Publication Date:

Tue Mar 27 00:00:00 EDT 2018

Research Org.:

Princeton Univ., NJ (United States)

Sponsoring Org.:

USDOE Office of Science (SC)

OSTI Identifier:

1540165

Alternate Identifier(s):

OSTI ID: 1429965

Grant/Contract Number:  

SC0008626; SC0009248

Resource Type:

Accepted Manuscript

Journal Name:

Journal of Chemical Physics

Additional Journal Information:

Journal Volume: 148; Journal Issue: 12; Journal ID: ISSN 0021-9606

Publisher:

American Institute of Physics (AIP)

Country of Publication:

United States

Language:

English

Subject:

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; chemistry; physics