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Locating Transition Zone in Phase Space.

Yao-Kun Lei1,2, Zhen Zhang3, Xu Han1,2

  • 1Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, 518055 Shenzhen, China.

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|September 22, 2022
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Summary
This summary is machine-generated.

A new machine learning method helps understand chemical reaction mechanisms by identifying transition states. This approach incorporates velocity effects and quantifies solvent influence, improving reaction rate predictions.

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Area of Science:

  • Chemical Dynamics
  • Computational Chemistry
  • Machine Learning Applications

Background:

  • Understanding reaction mechanisms is crucial for controlling chemical reactions.
  • Locating transition states (TSs) along a reaction coordinate (RC) is key but challenging for complex systems.
  • Existing methods struggle with high-dimensional systems and unbalanced data near TSs.

Purpose of the Study:

  • Develop a machine learning (ML) method to identify the reaction coordinate (RC) in complex chemical systems.
  • Incorporate the influence of velocity on the reaction process within the ML framework.
  • Quantify the impact of the reaction environment, specifically solvents, on reaction dynamics.

Main Methods:

  • A novel machine learning approach is developed to locate the transition zone in phase space.
  • The method defines a dividing surface with a high transmission coefficient, overcoming the unbalanced label problem.
  • Two measures are devised to quantify the influence of the reaction path and energy transfer from the environment.

Main Results:

  • The ML method effectively incorporates velocity effects and is scalable to large systems.
  • Solvents were found to assist reactions by performing work directly along the reactive mode.
  • A positive correlation exists between energy transfer efficiency into the reactive mode and the reaction rate.

Conclusions:

  • The developed ML method provides a robust way to identify reaction pathways and transition states in complex systems.
  • Solvent effects play a significant role in reaction kinetics, directly influencing the reactive mode.
  • This work offers a new computational tool for advancing the understanding and control of chemical reactions.