Citation: Zhang Jiawei, Chen Jianfu, Hu Peijun, Wang Haifeng. Identifying the composition and atomic distribution of Pt-Au bimetallic nanoparticle with machine learning and genetic algorithm[J]. Chinese Chemical Letters, ;2020, 31(3): 890-896. doi: 10.1016/j.cclet.2019.12.006 shu

Identifying the composition and atomic distribution of Pt-Au bimetallic nanoparticle with machine learning and genetic algorithm

a.
Key Laboratory for Advanced Materials, Research Institute of Industrial Catalysis and Centre for Computational Chemistry, School of Chemistry and Molecular Engineering, East China University of Science and Technology, Shanghai 200237, China
b.
School of Chemistry and Chemical Engineering, The Queen's University of Belfast, Belfast BT9 5AG, United Kingdom

hfwang@ecust.edu.cn

Received Date: 13 November 2019
Revised Date: 27 November 2019
Accepted Date: 2 December 2019
Available Online: 3 December 2019

Figures(7)

Bimetallic nanoparticles (A_mB_n) usually exhibit rich catalytic chemistry and have drawn tremendous attention in heterogeneous catalysis. However, challenged by the huge configuration space, the understanding toward their composition and distribution of A/B element is known little at the atomic level, which hinders the rational synthesis. Herein, we develop an on-the-fly training strategy combing the machine learning model (SchNet) with the genetic algorithm (GA) search technique, which achieve the fast and accurate energy prediction of complex bimetallic clusters at the DFT level. Taking the 38-atom Pt_mAu_38-m nanoparticle as example, the element distribution identification problem and the stability trend as a function of Pt/Au composition is quantitatively resolved. Specifically, results show that on the Pt-rich cluster Au atoms prefer to occupy the low-coordinated surface corner sites and form patch-like surface segregation patterns, while for the Au-rich ones Pt atoms tend to site in the core region and form the core-shell (Pt@Au) configuration. The thermodynamically most stable Pt_mAu_38-m cluster is Pt₆Au₃₂, with all the core-region sites occupied by Pt, rationalized by the stronger Pt-Pt bond in comparison with Pt-Au and Au-Au bonds. This work exemplifies the potent application of rapid global search enabled by machine learning in exploring the high-dimensional configuration space of bimetallic nanocatalysts.
- Density functional theory calculation,
- Machine learning,
- Genetic algorithm,
- Bimetallic nanoparticle,
- PtAu cluster
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1.
沈阳化工大学材料科学与工程学院沈阳 110142