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Accelerated Entropic Path Sampling with a Bidirectional Generative Adversarial Network.

Wook Shin1, Xinchun Ran1, Zhongyue J Yang1,2,3,4,5

  • 1Department of Chemistry, Vanderbilt University, Nashville, Tennessee 37235, United States.

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|May 3, 2023
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Summary
This summary is machine-generated.

We developed an accelerated entropic path sampling method using deep learning to efficiently compute entropy changes in chemical reactions. This significantly reduces computational cost while revealing "hidden entropic intermediates" in reaction dynamics.

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Area of Science:

  • Chemical Dynamics
  • Computational Chemistry
  • Statistical Mechanics

Background:

  • The role of entropy in chemical reaction dynamics is not well understood.
  • Previous entropic path sampling methods require substantial computational resources (approx. 2000 trajectories).

Purpose of the Study:

  • To develop an accelerated entropic path sampling approach for efficient computation of entropic profiles.
  • To investigate the role of entropy in chemical reaction dynamics and identify novel intermediates.

Main Methods:

  • Leveraged a deep generative model (bidirectional generative adversarial network-entropic path sampling) to enhance probability density function estimation.
  • Generated statistically indistinguishable pseudo-molecular configurations to reduce the number of required trajectories.
  • Benchmarked the method using cyclopentadiene dimerization and other reactions with post-transition state bifurcation.

Main Results:

  • Successfully reproduced reference entropic profiles with significantly fewer trajectories (124 vs. 2480 for cyclopentadiene dimerization).
  • Demonstrated the method's efficacy on various dimerization reactions.
  • Identified a "hidden entropic intermediate"—a dynamic species at a local entropic maximum without a free energy minimum.

Conclusions:

  • The bidirectional generative adversarial network-entropic path sampling method offers a computationally efficient alternative for calculating entropic profiles.
  • The discovery of hidden entropic intermediates provides new insights into reaction mechanisms and dynamics.