| Autor: |
Yao-Kun Lei, Zhen Zhang, Xu Han, Yi Isaac Yang, Jun Zhang, Yi Qin Gao |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Journal of chemical theory and computation. 18(10) |
| ISSN: |
1549-9626 |
| Popis: |
Understanding the reaction mechanism is required for better control of chemical reactions and is usually achieved by locating transition states (TSs) along a proper one-dimensional coordinate called reaction coordinate (RC). The identification of RC can be very difficult for high-dimensional realistic systems. A number of methods have been proposed to tackle this problem. A machine learning method is developed here to incorporate the influence of velocity on the reaction process. The method is also free of the unbalanced label problem resulting from the rather low fraction of configurations near the TS and can be easily extended to large systems. It locates the transition zone in the phase space and defines the dividing surface with a high transmission coefficient. Moreover, considering that the reaction environment can not only change the reaction path but also activate the reactive mode through energy transfer, we devise two measures to quantify the influence of these two factors on the reaction process and find that solvents can assist the reaction by directly doing work along the reactive mode. Not surprisingly, there is a positive correlation between the efficiency of energy transfer into the reactive mode and the reaction rate. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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