期刊
JOURNAL OF CHEMICAL THEORY AND COMPUTATION
卷 12, 期 12, 页码 6213-6226出版社
AMER CHEMICAL SOC
DOI: 10.1021/acs.jctc.6b00994
关键词
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资金
- Air Force Office of Scientific Research under a Basic Research Initiative [AFOSR FA9550-16-1-0141]
- NSF [CHE1351968]
- Direct For Mathematical & Physical Scien
- Division Of Chemistry [1351968] Funding Source: National Science Foundation
We first report a global optimization approach based on GPU accelerated Deep Neural Network (DNN) fitting, for modeling metal clusters at realistic temperatures. The seven-layer multidimensional and locally connected DNN is combined with limited-step Density Functional Theory (DFT) geometry optimization to reduce the time cost of full DFT local optimization, which is considered to be the most time-consuming step in global optimization. An algorithm based on bond length distribution analysis is used to efficiently sample the configuration space and generate random initial structures. A structure similarity measurement method based on depth-first search is used to identify duplicates. The performance of the new approach is examined by the application to the global minimum searching for Pt-9 and Pt-13. The ensemble-average representations of the two clusters are constructed based on all geometrically different isomers, on which the structure transition is predicted at low and high temperatures, for Pt-9 and Pt-13 clusters, respectively. Finally, the ensemble-averaged vertical ionization potential changes when temperature increases, and the property in conditions of catalysis can be different from that evaluated at the global minimum structure.
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