Developing General Reactive Element-Based Machine Learning Potentials as the Main Computational Engine for Heterogeneous Catalysis

doi:10.26434/chemrxiv-2024-r8l6j

	Developing General Reactive Element-Based Machine Learning Potentials as the Main Computational Engine for Heterogeneous Catalysis
	Yang, Changxi 1,2; Wu, Chenyu 1,3; Xie, Wenbo1 ; Xie, Daiqian 1; Hu, Peijun1,4
	2024-10-14
状态	已发表
摘要	Machine learning potentials (MLPs) have emerged as a promising technique to significantly enhance efficiency by replacing computationally expensive quantum mechanical calculations. However, developing truly universal MLPs remains challenging, as the consensus is that MLPs can only be used for similar structures that they have been trained on, while the vast and diverse chemical space is difficult to fully sample using the common system-dependent sampling methods. Here, our approach leverages a unique random exploration via imaginary chemicals optimization (REICO) strategy, which enables unbiased exploration of chemical space by focusing on atomic interactions. The resulting EMLP is inherently general and reactive, capable of accurately predicting elementary reactions without explicit structural or reaction pathway inputs. Benchmarked across various representative calculations of heterogeneous catalysis, our EMLP achieves quantitative agreement with density functional theory (DFT) calculations. This demonstrates the potential of EMLP as a powerful, general, and user-friendly tool for modeling complex chemical systems, paving the way to replace DFT calculations for large and intricate systems. Our approach is also applicable to broader fields such as materials science and molecular biology, representing a paradigm shift in MLPs-related research.
关键词	Machine learning potential heterogeneous catalysis reactions
语种	英语
DOI	10.26434/chemrxiv-2024-r8l6j
相关网址	查看原文
出处	chemRxiv
收录类别	PPRN.PPRN
WOS记录号	PPRN:109935989
WOS类目	Chemistry, Multidisciplinary
资助项目	National Natural Science Foundation of China (NSFC)[92045303] ; National Natural Science Foundation of China (NKRDPC)[2021YFA1500700]
文献类型	预印本
条目标识符	https://kms.shanghaitech.edu.cn/handle/2MSLDSTB/445530
专题	物质科学与技术学院物质科学与技术学院_PI研究组_胡培君组
通讯作者	Xie, Wenbo; Hu, Peijun
作者单位	1.ShanghaiTech Univ, Sch Phys Sci & Technol, Shanghai 201210, Peoples R China 2.East China Univ Sci & Technol, Res Inst Ind Catalysis, Ctr Computat Chem, Sch Chem & Mol Engn, Shanghai 200237, Peoples R China 3.Nanjing Univ, Sch Chem & Chem Engn, Key Lab Mesoscop Chem MOE, Nanjing 210023, Peoples R China 4.Queens Univ Belfast, Sch Chem & Chem Engn, Belfast BT9 5AG, North Ireland
推荐引用方式 GB/T 7714	Yang, Changxi,Wu, Chenyu,Xie, Wenbo,et al. Developing General Reactive Element-Based Machine Learning Potentials as the Main Computational Engine for Heterogeneous Catalysis. 2024.