Application of Machine-Learning Assisted Dynamics Simulations in Nano-Scale Catalysis 机器学习辅助的纳米催化反应动力学研究进展

Lin，Bo; Zhang，Shuangzhe; Li，Bai; Zhou，Chuan; Li，Lei

2023-02-01

10.14062/j.issn.0454-5648.20220923

Kuei Suan Jen Hsueh Pao/Journal of the Chinese Ceramic Society   影响因子和分区

0454-5648

As one of the important simulation methods in computational catalysis, molecular dynamics (MD) simulation plays an important role in understanding the catalytic mechanisms and is critical to the design of efficient and stable catalysts. Classical MD simulation with empirical potentials has a high computational efficiency but a limited accuracy, particularly for systems involving chemical reactions, and the accurate first-principle methods suffer from heavy computational costs and become unaffordable in most cases. The existing emerging machine-learning force field (MLFF) method is proven with affordable computational cost and first-principle-level accuracy. MLFF-assisted MD simulation can offer an effective approach for dynamics simulation in nanoscale catalysis. This review represented the fundamental principle of two main MLFF methods, i.e., the Behler-Parrinello atom-centered neural network method and the embedded-network-based deep potential. The applications of MLFF-assisted dynamic studies related to nano-scale catalysis (i.e., structure reconstruction and reaction processes in catalysis) were described. In addition, some possible future challenges of MLFF methods in dynamics simulation were also given.

machine learning force field molecular dynamics nanoscale catalysis reaction kinetics

EI

中文

第一 ; 通讯

20231513886078

Catalysis ; Computational chemistry ; Computational efficiency ; Machine learning ; Nanotechnology ; Reaction kinetics

Artificial Intelligence:723.4 ; Nanotechnology:761 ; Chemistry:801 ; Physical Chemistry:801.4 ; Chemical Reactions:802.2 ; Numerical Methods:921.6

2-s2.0-85152211848

Scopus

Shenzhen Key Laboratory of Micro/Nano-Porous Functional Materials (SKLPM),Department of Materials Science and Engineering,Southern University of Science and Technology,Shenzhen,Guangdong,518055,China

Lin，Bo,Zhang，Shuangzhe,Li，Bai,等. Application of Machine-Learning Assisted Dynamics Simulations in Nano-Scale Catalysis 机器学习辅助的纳米催化反应动力学研究进展[J]. Kuei Suan Jen Hsueh Pao/Journal of the Chinese Ceramic Society,2023,51(2):510-519.

Lin，Bo,Zhang，Shuangzhe,Li，Bai,Zhou，Chuan,&Li，Lei.(2023).Application of Machine-Learning Assisted Dynamics Simulations in Nano-Scale Catalysis 机器学习辅助的纳米催化反应动力学研究进展.Kuei Suan Jen Hsueh Pao/Journal of the Chinese Ceramic Society,51(2),510-519.

Lin，Bo,et al."Application of Machine-Learning Assisted Dynamics Simulations in Nano-Scale Catalysis 机器学习辅助的纳米催化反应动力学研究进展".Kuei Suan Jen Hsueh Pao/Journal of the Chinese Ceramic Society 51.2(2023):510-519.