Accessing complex reconstructed material structures with hybrid global optimization accelerated <i>via</i> on-the-fly machine learning-Reference-Cited by-同舟云学术

Accessing complex reconstructed material structures with hybrid global optimization accelerated via on-the-fly machine learning

Published:2023 Issue:33 Volume:14 Page:8777-8784
ISSN:2041-6520
Container-title:Chemical Science
language:en
Short-container-title:Chem. Sci.

Author:

Shi Xiangcheng¹²³⁴,Cheng Dongfang¹²³,Zhao Ran¹²³,Zhang Gong¹²³^ORCID,Wu Shican¹²³,Zhen Shiyu¹²³,Zhao Zhi-Jian¹²⁵³^ORCID,Gong Jinlong¹²⁵³⁶^ORCID

Affiliation:

1. School of Chemical Engineering and Technology, Key Laboratory for Green Chemical Technology of Ministry of Education, Tianjin University, Tianjin 300072, China

2. Collaborative Innovation Center of Chemical Science and Engineering (Tianjin), Tianjin 300072, China

3. National Industry-Education Platform of Energy Storage, Tianjin University, 135 Yaguan Road, Tianjin 300350, China

4. Department of Chemistry, National University of Singapore, 3 Science Drive 3, Singapore, 117543, Republic of Singapore

5. Haihe Laboratory of Sustainable Chemical Transformations, Tianjin 300192, China

6. Joint School of National University of Singapore and Tianjin University, International Campus of Tianjin University, Binhai New City, Fuzhou 350207, Fujian, China

Abstract

This paper describes a multi-tribe hybrid evolutionary algorithm that combines differential evolution and genetic algorithms for surface structural optimization accelerated by an on-the-fly machine learning calculator.

Funder

National Natural Science Foundation of China

National Key Research and Development Program of China

Publisher

Royal Society of Chemistry (RSC)

Subject

General Chemistry

Link

http://pubs.rsc.org/en/content/articlepdf/2023/SC/D3SC02974C

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