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Explore the Chemical Space of Linear Alkanes Pyrolysis via Deep Potential Generator

preprint
submitted on 10.09.2020 and posted on 10.09.2020 by Jinzhe Zeng, Linfeng Zhang, Han Wang, Tong Zhu

Reactive molecular dynamics (MD) simulation is a powerful tool to study the reaction mechanism of complex chemical systems. Central to the method is the potential energy surface (PES) that can describe the breaking and formation of chemical bonds. The development of PES of both accurate and efficent has attracted significant effort in the past two decades. Recently developed Deep Potential (DP) model has the promise to bring ab initio accuracy to large-scale reactive MD simulations. However, for complex chemical reaction processes like pyrolysis, it remains challenging to generate reliable DP models with an optimal training dataset. In this work, a dataset construction scheme for such a purpose was established. The employment of a concurrent learning algorithm allows us to maximize the exploration of the chemical space while minimize the redundancy of the dataset. This greatly reduces the cost of computational resources required by ab initio calculations. Based on this method, we constructed a dataset for the pyrolysis of n-dodecane, which contains 35,496 structures. The reactive MD simulation with the DP model trained based on this dataset revealed the pyrolysis mechanism of n-dodecane in detail, and the simulation results are in good agreement with the experimental measurements. In addition, this dataset shows excellent transferability to different long-chain alkanes. These results demonstrate the advantages of the proposed method for constructing training datasets for similar systems.

History

Email Address of Submitting Author

tzhu@lps.ecnu.edu.cn

Institution

School of Chemistry and Molecular Engineering, East China Normal University

Country

China

ORCID For Submitting Author

0000-0001-7472-3736

Declaration of Conflict of Interest

No conflict of interest.

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