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Reaction prediction via atomistic simulation: from quantum mechanics to machine learning.

Pei-Lin Kang1, Zhi-Pan Liu1

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Summary

Predicting chemical reactions computationally is a major goal. This review highlights atomistic simulations and machine learning potentials, like the stochastic surface walking neural network (SSW-NN), for unbiased reaction exploration and prediction.

Keywords:
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Area of Science:

  • Chemistry
  • Computational Chemistry
  • Materials Science

Background:

  • Predicting chemical reactions computationally is a long-standing challenge in chemistry.
  • Current methods for reaction prediction include rate determination and reaction space exploration.
  • Atomistic simulation methods are crucial for understanding reaction mechanisms.

Purpose of the Study:

  • To review the theory and computational methods for chemical reaction prediction.
  • To focus on atomistic simulation methods enhanced by machine learning potentials.
  • To present the stochastic surface walking global pathway sampling based on neural network potentials (SSW-NN) for unbiased reaction exploration.

Main Methods:

  • Overview of chemical reaction theory.
  • Computational methods for reaction rate estimation.
  • Atomistic simulations utilizing machine learning potentials.
  • Stochastic Surface Walking global pathway sampling with Neural Network potentials (SSW-NN).

Main Results:

  • SSW-NN enables unbiased and automated exploration of complex reaction systems.
  • The SSW-NN method has been developed and applied since 2013.
  • Successful applications demonstrated for molecular and heterogeneous catalytic reactions.

Conclusions:

  • Machine learning potentials significantly advance atomistic simulations for reaction exploration.
  • SSW-NN provides a powerful tool for automated and unbiased prediction of chemical reactions.
  • The presented examples illustrate the potential of SSW-NN for diverse reaction systems.