Viana M L, dos Reis D D, Soares E A, Van Hove M A, Moritz W, de Carvalho V E
Departamento de Ciências Exatas, Instituto Federal Minas Gerais-Campus Bambuí, Bambuí, Minas Gerais, Brazil.
J Phys Condens Matter. 2014 Jun 4;26(22):225005. doi: 10.1088/0953-8984/26/22/225005. Epub 2014 May 13.
Low Energy Electron Diffraction (LEED) is one of the most powerful experimental techniques for surface structure analysis but until now only a trial-and-error approach has been successful. So far, fitting procedures developed to optimize structural and nonstructural parameters-by minimization of the R-factor-have had a fairly small convergence radius, suitable only for local optimization. However, the identification of the global minimum among the several local minima is essential for complex surface structures. Global optimization methods have been applied to LEED structure determination, but they still require starting from structures that are relatively close to the correct one, in order to find the final structure. For complex systems, the number of trial structures and the resulting computation time increase so rapidly that the task of finding the correct model becomes impractical using the present methodologies. In this work we propose a new search method, based on Genetic Algorithms, which is able to determine the correct structural model starting from completely random structures. This method-called here NGA-LEED for Novel Genetic Algorithm for LEED-utilizes bond lengths and symmetry criteria to select reasonable trial structures before performing LEED calculations. This allows a reduction of the parameter space and, consequently of the calculation time, by several orders of magnitude. A refinement of the parameters by least squares fit of simulated annealing is performed only at some intermediate stages and in the final step. The method was successfully tested for two systems, Ag(1 1 1)(4 × 4)-O and Au(1 1 0)-(1 × 2), both in theory versus theory and in theory versus experiment comparisons. Details of the implementation as well as the results for these two systems are presented.
低能电子衍射（LEED）是用于表面结构分析的最强大的实验技术之一，但到目前为止，只有反复试验的方法取得了成功。到目前为止，为通过最小化R因子来优化结构和非结构参数而开发的拟合程序收敛半径相当小，仅适用于局部优化。然而，在几个局部最小值中识别全局最小值对于复杂的表面结构至关重要。全局优化方法已应用于LEED结构确定，但它们仍然需要从相对接近正确结构的结构开始，以便找到最终结构。对于复杂系统，试验结构的数量和由此产生的计算时间增长如此之快，以至于使用当前方法找到正确模型的任务变得不切实际。在这项工作中，我们提出了一种基于遗传算法的新搜索方法，该方法能够从完全随机的结构开始确定正确的结构模型。这种方法——这里称为NGA-LEED（用于LEED的新型遗传算法）——在进行LEED计算之前，利用键长和对称性标准来选择合理的试验结构。这使得参数空间以及计算时间减少了几个数量级。仅在一些中间阶段和最后一步通过模拟退火的最小二乘法拟合对参数进行细化。该方法在理论与理论以及理论与实验比较中，针对Ag(1 1 1)(4×4)-O和Au(1 1 0)-(1×2)这两个系统进行了成功测试。本文介绍了实现细节以及这两个系统的结果。
