用于晶体结构预测的稳态遗传算法的开放科学网格实施方案

IF 3.1 3区 计算机科学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Kristal N. Varela , Gabriel I. Pagola , Albert M. Lund , Marta B. Ferraro , Anita M. Orendt , Julio C. Facelli
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引用次数: 0

摘要

在本文中,我们报告了在晶体结构预测系统 MGAC 中实施和测试算法变化的情况,这些变化是为了使该系统具有可扩展性,并能够利用开放科学网格(OSG)等重要的分布式资源。这些变化包括:采用稳态遗传算法(GA);采用更通用的 GA 基因组定义,从而无需对 230 个可能的空间群逐一进行搜索;使用量子 Espresso(QE)中实施的密度泛函理论与弥散校正(DFT-D)来计算晶体能量。我们在下文中将 MGAC 的这一实施方案命名为 MGAC-QE-OSG,并在甲醇和乙醇这两个测试案例中对其性能进行了演示。在这两种情况下,MGAC-QE-OSG 都能找到这些化合物的实验结构。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An open science grid implementation of the steady state genetic algorithm for crystal structure prediction

In this paper we report the implementation and testing of algorithmic changes that have been implemented in MGAC, a crystal structure prediction system, to make it scalable and amenable to take advantage of such significant distributed resources as the Open Science Grid (OSG). The changes include the adoption of a steady state Genetic Algorithm (GA) and the adoption of a more general definition of the GA genome that eliminates the need of searching individually for each of the 230 possible space groups and the use of the Density Functional Theory with dispersion correction (DFT-D) as implemented in Quantum Espresso (QE) to calculate crystal energies. The performance of this implementation of MGAC, which in the following we label as MGAC-QE-OSG, is demonstrated for two test cases methanol and ethanol. In both cases the MGAC-QE-OSG can find the experimental structures of these compounds.

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来源期刊
Journal of Computational Science
Journal of Computational Science COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS-COMPUTER SCIENCE, THEORY & METHODS
CiteScore
5.50
自引率
3.00%
发文量
227
审稿时长
41 days
期刊介绍: Computational Science is a rapidly growing multi- and interdisciplinary field that uses advanced computing and data analysis to understand and solve complex problems. It has reached a level of predictive capability that now firmly complements the traditional pillars of experimentation and theory. The recent advances in experimental techniques such as detectors, on-line sensor networks and high-resolution imaging techniques, have opened up new windows into physical and biological processes at many levels of detail. The resulting data explosion allows for detailed data driven modeling and simulation. This new discipline in science combines computational thinking, modern computational methods, devices and collateral technologies to address problems far beyond the scope of traditional numerical methods. Computational science typically unifies three distinct elements: • Modeling, Algorithms and Simulations (e.g. numerical and non-numerical, discrete and continuous); • Software developed to solve science (e.g., biological, physical, and social), engineering, medicine, and humanities problems; • Computer and information science that develops and optimizes the advanced system hardware, software, networking, and data management components (e.g. problem solving environments).
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