Molecular Dynamics and Machine Learning in Catalysts

Jingchao Zhang,Yang Hong,Bo Hou,Yang Zhu,Yongqiang Wu,Yanan Yue,Cen Chen,Wenxiang Liu

doi:10.3390/catal11091129

Abstract

Given the importance of catalysts in the chemical industry, they have been extensively investigated by experimental and numerical methods. With the development of computational algorithms and computer hardware, large-scale simulations have enabled influential studies with more atomic details reflecting microscopic mechanisms. This review provides a comprehensive summary of recent developments in molecular dynamics, including ab initio molecular dynamics and reaction force-field molecular dynamics. Recent research on both approaches to catalyst calculations is reviewed, including growth, dehydrogenation, hydrogenation, oxidation reactions, bias, and recombination of carbon materials that can guide catalyst calculations. Machine learning has attracted increasing interest in recent years, and its combination with the field of catalysts has inspired promising development approaches. Its applications in machine learning potential, catalyst design, performance prediction, structure optimization, and classification have been summarized in detail. This review hopes to shed light and perspective on ML approaches in catalysts.

Highlights

Catalysts have attracted growing interest due to their unique effects on chemical reactions
The first part will review the study of the ab initio molecular dynamics (AIMD) method and the ReaxFF molecular dynamics in calculating different reactions, including the growth of carbon materials, dehydrogenation, hydrogenation, oxidation reaction, segregation, and restructuring
Unlike the AIMD approach, in which the electronic structure is solved directly, ReaxFF molecular dynamics is based on the reactive force field

Summary

Introduction

Catalysts have attracted growing interest due to their unique effects on chemical reactions. The AIMD approach solves the Schrödinger equation by various approximations [17] It combines quantum mechanics and molecular dynamics that can accurately describe the electronic structure and atomic motion. The calculated speed is one order of magnitude slower than classical force fields due to the charge equilibration calculations at each timestep and the modeling of bond formation and breaking [21], ReaxFF is still a useful molecular dynamics method to study chemical reactions that is still evolving [22,23]. The first part will review the study of the AIMD method and the ReaxFF molecular dynamics in calculating different reactions, including the growth of carbon materials, dehydrogenation, hydrogenation, oxidation reaction, segregation, and restructuring. The second part will provide an overview of the ML methods report of the application of machine learning in catalysis, including machine learning potentials, new catalyst discovery and design, and some helpful machine learning community projects

Introduction of Molecular Dynamics

Ab initio Molecular Dynamics

Reactive Force Field Molecular Dynamics

Application of AIMD and ReaxFF

Dehydrogenation and Hydrogenation

Segregation and Restructuring

Discussion

Machine Learning in Catalysts

Introduction of Methods

Machine Learning Potentials

The Development of Descriptors

Conclusions and Outlook