The XtalOpt Evolutionary Algorithm for Crystal Structure Prediction
The XtalOpt Evolutionary Algorithm for Crystal Structure Prediction
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DOI：
10.1021/acs.jpcc.0c09531
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发表时间：
2021-01-28
影响因子：
3.7
通讯作者：
Zurek, Eva
中科院分区：
文献类型：
--
作者：
Falls, Zackary;Avery, Patrick;Zurek, Eva
先验晶体结构预测领域取得了重大进展，近年来取得了许多显著的成功。在此，我们简要地概述了在不需要实验信息的情况下寻找全局最小值结构和有趣的局部最小值的方法。重点是描述我们小组为此目的开发的XtalOpt进化算法（EA）。XtalOpt是在知名的开源许可下发布的，EA搜索可以通过阿伏伽德罗化学编辑器和可视化器进行分析。我们描述了新的算法的发展，使得预测更复杂的晶体晶格的结构成为可能。基准测试清楚地说明了新的发展如何提高成功率并加速发现全局最小结构。最后，我们描述了如何使用XtalOpt来预测在压力下具有高温超导倾向的新型三元氢化物。
Significant progress has been made in the field of a priori crystal structure prediction, with a number of recent remarkable success stories. Herein, we briefly outline the methods that have been developed for finding the global minimum structure and interesting local minima without the need for experimental information. Focus is placed on describing the XtalOpt evolutionary algorithm (EA) developed in our group toward this end. XtalOpt is published under well-known open-source licenses, and the EA searches can be analyzed via the Avogadro chemical editor and visualizer. We describe new algorithmic developments that have made it possible to predict the structures of ever-more complex crystalline lattices. Benchmark tests, which clearly illustrate how the new developments improve the success rate and accelerate the discovery of the global minimum structure, are performed. Finally, we describe how XtalOpt has been employed to predict novel ternary hydrides that have the propensity for high-temperature superconductivity under pressure.