Hierarchical structures and magnetism of Co clusters: a perspective from integration of deep learning and a hybrid differential evolution algorithm-Reference-Cited by-同舟云学术

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Hierarchical structures and magnetism of Co clusters: a perspective from integration of deep learning and a hybrid differential evolution algorithm

Published:2024 Issue: Volume: Page:
ISSN:2040-3364
Container-title:Nanoscale
language:en
Short-container-title:Nanoscale

Author:

Yang Wei-Hua¹,Yu Fang-Qi¹,Guo Zi-Wen²,Huang Rao¹^ORCID,Chen Jun-Ren³,Gao Feng-Qiang³,Shao Gui-Fang⁴,Liu Tun-Dong⁴,Wen Yu-Hua¹^ORCID

Affiliation:

1. Department of Physics, Xiamen University, Xiamen 361005, China

2. Institute of Artificial Intelligence, Xiamen University, Xiamen 361005, China

3. Xiamen University Tan Kah Kee College, Zhangzhou, 363105, China

4. Pen-Tung Sah Institute of Micro-Nano Science and Technology, Xiamen University, Xiamen 361102, China

Abstract

Combining a well-trained deep potential (DP) model and a high-efficiency hybrid differential evolution (HDE) algorithm to search for the lowest-energy structures of CoN (N = 11–50) clusters.

Funder

Natural Science Foundation of Fujian Province

Fundamental Research Funds for the Central Universities

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2024/NR/D4NR02431A

Reference59 articles.

1. Enhanced stability of Pt nanoparticle electrocatalysts for fuel cells

2. Electronic and Atomic Structure, and Magnetism of Transition-Metal Clusters

3. Reactivity of Metal Clusters

4. Evolution of atomic and electronic structure of Pt clusters: Planar, layered, pyramidal, cage, cubic, and octahedral growth

5. Size and structure effects of PtN (N = 12 − 13) clusters for the oxygen reduction reaction: First-principles calculations

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