Evolutionary reinforcement learning of dynamical large deviations-Reference-Cited by-同舟云学术

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Evolutionary reinforcement learning of dynamical large deviations

Published:2020-07-28 Issue:4 Volume:153 Page:044113
ISSN:0021-9606
Container-title:The Journal of Chemical Physics
language:en
Short-container-title:J. Chem. Phys.

Author:

Whitelam Stephen¹^ORCID,Jacobson Daniel²,Tamblyn Isaac³⁴^ORCID

Affiliation:

1. Molecular Foundry, Lawrence Berkeley National Laboratory, 1 Cyclotron Road, Berkeley, California 94720, USA

2. Division of Chemistry and Chemical Engineering, California Institute of Technology, Pasadena, California 91125, USA

3. National Research Council of Canada, Ottawa, Ontario K1N 5A2, Canada

4. Vector Institute for Artificial Intelligence, Toronto, Ontario M5G 1M1, Canada

Funder

U.S. Department of Energy

NRC Canada

Publisher

AIP Publishing

Subject

Physical and Theoretical Chemistry,General Physics and Astronomy

Link

http://aip.scitation.org/doi/am-pdf/10.1063/5.0015301

Reference75 articles.

1. Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces

2. Deep learning and the Schrödinger equation

3. Machine learning and data science in materials design: a themed collection

4. Constructing first-principles phase diagrams of amorphous LixSi using machine-learning-assisted sampling with an evolutionary algorithm

5. Density anomaly of water at negative pressures from first principles

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1. Combining Reinforcement Learning and Tensor Networks, with an Application to Dynamical Large Deviations;Physical Review Letters;2024-05-07

2. Entropy regularized reinforcement learning using large deviation theory;Physical Review Research;2023-05-10

3. Phase coexistence implications of violating Newton’s third law;The Journal of Chemical Physics;2023-04-24

4. Landau theory for finite-time dynamical phase transitions;New Journal of Physics;2023-02-01

5. Complex Oxides for Brain‐Inspired Computing: A Review;Advanced Materials;2022-11-30

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