Inverted List Kinetic Monte Carlo with Rejection Applied to Directed Self-Assembly of Epitaxial Growth

作     者：Michael A.Saum Tim P.Schulze Christian Ratsch 

作者机构：Department of MathematicsUniversity of TennesseeKnoxvilleTennessee 37996USA Department of Mathematics and Institute for Pure and Applied MathematicsUniversity of California in Los AngelesLos AngelesCalifornia 90095USA 

出 版 物：《Communications in Computational Physics》 (计算物理通讯（英文）)

年 卷 期：2009年第6卷第8期

页      面：553-564页

核心收录：

学科分类：07[理学] 0809[工学-电子科学与技术（可授工学、理学学位）] 070205[理学-凝聚态物理] 08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 080502[工学-材料学] 0704[理学-天文学] 0702[理学-物理学] 

基　　金：MAS was supported by a grant from DOE(DE-FG02-03ER2558) TPS was supported by grants from DOE(DE-FG02-03ER2558)and NSF(NSF-DMS-0707443) 

主　　题：Epitaxial growth kinetic Monte Carlo binary-tree search 

摘      要：We study the growth of epitaxial thin films on pre-patterned substrates that influence the surface diffusion of subsequently deposited material using a kinetic Monte Carlo algorithm that combines the use of inverted lists with *** resulting algorithm is well adapted to systems with spatially heterogeneous hopping *** evaluate the algorithm’s performance we compare it with an efficient,binary-tree based algorithm.A key finding is that the relative performance of the inverted list algorithm improves with increasing system size.