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加速神经网络势能面的构建:一种杂化的训练算法
张耀龙,周雪瑶,蒋彬*
作者单位E-mail
张耀龙 中国科学技术大学化学物理系, 合肥 230026  
周雪瑶 中国科学技术大学化学物理系, 合肥 230026  
蒋彬* 中国科学技术大学化学物理系, 合肥 230026 bjiangch@ustc.edu.cn 
摘要:
关键词:  
DOI:10.1063/1674-0068/30/cjcp1711212
分类号:
基金项目:This work was supported by the National Key R&D Program of China (No.2017YFA0303500), National Natural Science Foundation of China (No.91645202, No.21722306, and No.21573203), Fundamental Research Funds for the Central Universities of China (No.WK2060190082 and No.WK2340000078) Calculations have been done at the Supercomputing Center of USTC.
Accelerating the Construction of Neural Network Potential Energy Surfaces: A Fast Hybrid Training Algorithm
Yao-long Zhang,Xue-yao Zhou,Bin Jiang*
Abstract:
Machine learning approaches have been promising in constructing high-dimensional potential energy surfaces (PESs) for molecules and materials. Neural networks (NNs) are one of the most popular such tools because of its simplicity and efficiency. The training algorithm for NNs becomes essential to achieve a fast and accurate fit with numerous data. The Levenberg-Marquardt (LM) algorithm has been recognized as one of the fastest and robust algorithms to train medium sized NNs and widely applied in recent NN based high quality PESs. However, when the number of ab initio data becomes large, the efficiency of LM is limited, making the training time consuming. Extreme learning machine (ELM) is a recently proposed algorithm which determines the weights and biases of a single hidden layer NN by a linear solution and is thus extremely fast. It, however, does not produce sufficiently small fitting error because of its random nature. Taking advantages of both algorithms, we report a generalized hybrid algorithm in training multilayer NNs. Tests on H+H2 and CH4+Ni(111) systems demonstrate the much higher efficiency of this hybrid algorithm (ELM-LM) over the original LM. We expect that ELM-LM will find its widespread applications in building up high-dimensional NN based PESs.
Key words:  Potential energy surface  Reaction dynamics  Neural networks