Physically Compatible Machine Learning Study on the Pt–Ni Nanoclusters
| Autor: | Liang Liu, Xi Zhang, Huijie Zhen, Siyan Gao, Zezhou Lin, Xiaolin Li |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Materials science
Basis (linear algebra) business.industry Binary number 02 engineering and technology 010402 general chemistry 021001 nanoscience & nanotechnology Machine learning computer.software_genre Space (mathematics) 01 natural sciences Bond order 0104 chemical sciences Nanoclusters Dimension (vector space) Structural stability General Materials Science Density functional theory Artificial intelligence Physical and Theoretical Chemistry 0210 nano-technology business computer |
| Zdroj: | The Journal of Physical Chemistry Letters. 12:1573-1580 |
| ISSN: | 1948-7185 |
| DOI: | 10.1021/acs.jpclett.0c03600 |
| Popis: | Pt-Ni alloy nanoclusters are essential for high-performance catalysis, and the full description for the finite temperature properties is highly desired. Here we developed an efficient machine learning method to evaluate the accurate structure-stability correspondence in a Pt(85-x)-Nix nanocluster over the structural space with a dimension of 3.84 × 1025. On the basis of the physical model and big-data analysis, for the first time, we demonstrated that the segregation-extent bond order parameter (BOP) and the shell-resolved undercoordination ratio play the key roles in the structural stability. This a priori knowledge extremely reduced the computational costs and enhanced the accuracies. With the 500-sample train data set generated by density functional theory (DFT)-level geometry optimizations, we fit the machine-learning excess energy potential and verified the mean-square-error is |
| Databáze: | OpenAIRE |
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