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.
参考文献
| [1] | 阮德水,李卫萍.金的化学[J].高等函授学报（自然科学版）,2000(01):25-29. |
| [2] | 董守安.纳米技术中的金元素[J].贵金属,2003(01):54-61. |
| [3] | Skriver H L;Rosengaard N M .Surface energy and work function of elemental metals[J].Physical Review B,1992,46(11):7157-7168. |
| [4] | Daw M S;Baskes M I .Embedded-atom method: derivation and application to impurities, surface, and other defects in metals[J].Physical Review B,1984,29(12):6443-6453. |
| [5] | Johnson R A .Alloy models with the embedded-atom method[J].Physical Review B,1989,39(17):12554-12559. |
| [6] | Le-Hua Qi;Jun-Jie Hou;Pei-Ling Cui;He-Jun Li .Research on prediction of the processing parameters of liquid extrusion by BP network[J].Journal of Materials Processing Technology,1999(1/3):232-237. |
| [7] | Basheer L A .Artificial neural network:fundamentals,computing,design,and application[J].Journal of Microbiological Methods,2000,43:3-31. |
| [8] | Joines J A;White M W.Improved generalization using robust cost functions[A].New York:IEEE Press,1992:911-918. |
| [9] | Hagan M.T.;Menhaj M.B. .Training feedforward networks with the Marquardt algorithm[J].IEEE Transactions on Neural Networks,1994(6):989-993. |
| [10] | 张智星;孙春在;水谷英二.神经--模糊和软计算[M].西安:西安交通大学出版社,1998:156-215. |
| [11] | Hech-Nielsen R.Neurocomputing[M].Massachusetts: Addison Wesley Publishing Company,1991 |
| [12] | 张建民,徐可为,马飞.用改进嵌入原子法计算Cu晶体的表面能[J].物理学报,2003(08):1993-1999. |
| [13] | de Boer F R;Boom R;Mattens W C M.Cohesion in metals[M].Amsterdam, North-Holland:Elsevier Science,1988:716. |
| [14] | Foiles S M;Baskes M I;Daw M S .Embedded atom method functions for the FCC metals Cu, Ag, Au,Ni, Pd, Pt, and their alloys[J].Physical Review B,1986,33(12):7983-7990. |
| [15] | 闻立时.固体材料界面研究的物理基础[M].北京:科学出版社,1991:21. |
上一张
下一张
上一张
下一张
计量
- 下载量()
- 访问量()
文章评分
- 您的评分：
-
10%
-
20%
-
30%
-
40%
-
50%