(福州大学 材料科学与工程学院，福州350002)
摘 要: 利用嵌入原子模型， 采用分子动力学方法计算了贵金属Au低指数晶面及部分简单高指数晶面的表面能。 同时， 采用Levenberg-Marquardt 算法， 建立了Au表面能的BP神经网络模型； 结合分子动力学模型的计算数据， 通过大量数据的自学习训练， 完成神经网络模型对Au高指数晶面表面能的预测。 计算结果表明： 该方法具有较高的预测精度， 能正确预言低指数晶面表面能的排序， Au各晶面的表面能随其晶面与(111)密排面夹角的增大呈现先增大后减小的特点。
关键字: 表面能； 嵌入原子势； 人工神经网络；Levenberg-Marquardt算法
molecular dynamics combined with neural networks
（ School of Materials Science and Engineering, Fuzhou University, Fuzhou 350002, China）
Abstract: Via embedded-atom model and molecular dynamics simulation, the surface energies of three low-index and some high-index planes were calculated for precious metal Au, and the error back-propagation network (BP) developed by Levenberg-Marquardt algorithm was adopted. Combining the data calculated with the molecular dynamics model, a great deal of data were trained many times and compared with the calculated data, and the prediction of high-index surface energy was performed. The results show that the method has high predicting accuracy. The order of the three low-index planes was predicted exactly. The surface energies on the other planes show a tendency that first increasing and then decreasing with angle between the planes and (111) plane increasing.
Key words: surface energy; embedded-atom potential; artificial neural network; Levenberg-Marquardt algorithm
