10计算材料物理-第四章.pdf

计算材料物理 专题专题 结构搜索和预测结构搜索和预测2 Why is Structure Prediction Hard Energy Atomic positions Local minima Global minimum True structure Accurate Potential Energy Surface ab initio Huge number of local minima how 随机抽样方法 Random Sampling methods involve in the random generation of a large number of structures on PES AIRSS ab initio random structure searching http www cmmp ucl ac uk ajm airss airss html http iopscience iop org 0953 8984 23 5 053201 翻越势垒方法 模拟退火算法 simulated annealing SA 盆地跳算法 Basin hopping minima hopping metadynamics algorithm 模拟退火算法 Basin hopping minima hopping metadynamics 演化 进化 算法 遗传算法 genetic algorithms GA 粒子群优化算法 particle swarm optimization PSO 蚁群优化算法 ant colony optimization ACO 遗传算法 Swarm Intelligence 群体智能 Swarm可被描述为一些相互作用相邻个体的集合体 蜂 群 蚁群 鸟群都是Swarm的典型例子 鱼聚集成群可以有效地逃避捕食者 因为任何一只鱼发 现异常都可带动整个鱼群逃避 蚂蚁成群则有利于寻找食物 因为任一只蚂蚁发现食物 都可带领蚁群来共同搬运和进食 一只蜜蜂或蚂蚁的行为能力非常有限 它几乎不可能独 立存在于自然世界中 而多个蜜蜂或蚂蚁形成的Swarm 则具有非常强的生存能力 且这种能力不是通过多个个 体之间能力简单叠加所获得的 社会性动物群体所拥有的这种特性能帮助个体很好地适 应环境 个体所能获得的信息远比它通过自身感觉器官 所取得的多 其根本原因在于个体之间存在着信息交互 能力 粒子群优化算法 Particle Swarm Optimization 1995年由J Kennedy R C Eberhart等人提出 该算法最初是受到鸟群活动的规律性启发 进而利用群体 智能建立的一个简化模型 粒子群优化算法利用群体中的 个体对信息的共享使整个群体的运动在问题求解空间中产 生从无序到有序的演化过程 从而获得最优解 PSO同遗传算法类似 是一种基于迭代的优化算法 系统 初始化为一组随机解 通过迭代搜寻最优值 但是它没有 遗传算法用的交叉 crossover 以及变异 mutation 而是 粒子在解空间追随最优的粒子进行搜索 同遗传算法比较 PSO的优势在于简单容易实现并且没有许多参数需要调整 目前已广泛应用于函数优化 神经网络训练 模糊系统控 制以及其他遗传算法的应用领域 结构搜索和预测程序 AIRSS Ab initio Random Structure Searching GASP Genetic Algorithm for Structure and Phase Prediction CALYPSO Crystal structure AnaLYsis by Particle Swarm Optimization USPEX Universal Structure Predictor Evolutionary Xtallography http www cmmp ucl ac uk ajm airss airss html Chris J Pickard http avogadro cc wiki Main Page David C Lonie Eva Zurek XtalOpt An Open Source Evolutionary Algorithm for Crystal Structure Prediction Computer Physics Communications 182 2011 pp 372 387 XtalOpt is a free and truly open source evolutionary algorithm designed to predict crystal structures It is implemented as an extension to the Avogadro molecular editor XtalOpt runs on a workstation and supports using GULP VASP pwSCF Quantum ESPRESSO and CASTEP for geometry optimizations State University of New York at Buffalo The Genetic algorithm for structure prediction GASP predicts the structure and composition of stable and metastable phases of crystals molecules atomic clusters and defects from first principles The GASP program is interfaced to many energy codes including VASP LAMMPS MOPAC Gulp JDFTx and can efficiently run on parallel architectures CALYPSO Crystal structure AnaLYsis by Particle Swarm Optimization is an efficient structure prediction method and its same name computer software The CALYPSO package is protected by the Copyright Protection Center of China with the registration No 2010SR028200 and classification No 61000 7500 Freely distributed on Academic use under the regulations termed in the CALYPSO LICENCE 朱黎 吕健 王彦超 马琰铭 教授 吉林大学 超硬材料国家重点实验室 CALYPSO WHAT IS THE FEATURE Predictions of the energetically stable metastable structures at given chemical compositions and external conditions e g pressure for clusters 2D layers surfaces and 3D crystals Design of novel functional materials e g superhard materials Options for the structural evolutions using global or local PSO Structure searches with automatic variation of chemical compositions Structure predictions with fixed cell parameters or fixed space groups or fixed molecules CALYPSO is currently interfaced with GAUSSIAN DFTB VASP CASTEP Quantum Espresso GULP SIESTA and CP2K codes The interface with other total energy codes can also be implemented by users request History of CALYPSO The CALYPSO team independently initialized the idea of applying PSO algorithm into structure prediction in 2006 Ma and Wang first application of PSO algorithm into structure prediction of 3D crystals by Wang Lv Zhu Lyakhov Oganov Valle 2010 Features of the USPEXFeatures of the USPEX initialization using fully random approach or using space groups and cell splitting techniques Lyakhov Oganov Valle 2010 on the flight analysis of results determination of space groups and output in CIF format calculation of the hardness order parameters etc prediction of the structure of nanoparticles and surface reconstructions restart facilities enabling calculations to be continued from any point along the evolutionary trajectory powerful visualization and analysis techniques implemented in the STM4 code by M Valle fully interfaced with USPEX Features of the USPEXFeatures of the USPEX USPEX is interfaced with VASP SIESTA GULP DMACRYS CP2k QuantumEspresso codes Interfacing with other codes is easy submission of jobs from local workstation to remote clusters and supercomputers is possible options for structure prediction using the USPEX algorithm default random sampling corrected particle swarm optimization evolutionary metadynamics minima hopping like algorithm Capabilities to predict phase transition mechanisms using evolutionary metadynamics variable cell NEB method and TSP method options to optimize physical properties other than the energy e g hardness Lyakhov Oganov 2011 density Zhu et al 2011 and various electronic properties methodologies in USPEX Oganov A R Glass C W 2006 Crystal structure prediction using evolutionary algorithms principles and applications J Chem Phys 124 art 244704 pdf file Glass C W Oganov A R Hansen N 2006 USPEX evolutionary crystal structure prediction Comp Phys Comm 175 713 720 pdf file Oganov A R Ma Y Lyakhov A O Valle M Gatti C 2010 Evolutionary crystal structure prediction as a method for the discovery of miner