StructOpt: A modular materials structure optimization suite incorporating experimental data and simulated energies

Maldonis, Jason J.; Xu, Zhongnan; Song, Zhewen; Yu, Min; Mayeshiba, Tam; Voyles, Paul M.

doi:10.1016/j.commatsci.2018.12.052

Cited by 7 publications

(6 citation statements)

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“…Searching for the likely polymorphs of molecular crystals has its own considerations; for further information, we refer the reader to refs − . We also do not describe CSP methods that make use of information stored in large materials databases either explicitly, − ,, or by building machine learning models, − nor those that require experimental information. ,, …”

Section: Computational Detailsmentioning

confidence: 99%

“…We also do not describe CSP methods that make use of information stored in large materials databases either explicitly, [37][38][39]91,92 or by building machine learning models, 93−96 nor those that require experimental information. 37,97,98 Random Searching. In the simplest CSP method all 3N + 3 degrees of freedom of a user-defined number of crystals are generated randomly, and the structures are optimized to the nearest local minimum, as illustrated in Figure 3a.…”

Section: ■ Computational Detailsmentioning

confidence: 99%

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The XtalOpt Evolutionary Algorithm for Crystal Structure Prediction

et al. 2020

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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.

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Section: Computational Detailsmentioning

confidence: 99%

Section: ■ Computational Detailsmentioning

confidence: 99%

The XtalOpt Evolutionary Algorithm for Crystal Structure Prediction

et al. 2020

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“…The conventional approach is to include simple empirical potentials e.g. to constrain bond lengths and angles 4 , but newer frameworks anticipate the possibility for incorporating calculations based on higher-level theory 8 . A particularly exciting opportunity is the interface with efficient machine-learned potentials that allow energy calculations with density functional theory accuracy even for large atomistic configurations 9 .…”

Section: Making Realistic Models Of Complex Materialsmentioning

confidence: 99%

Opportunities and challenges in understanding complex functional materials

Goodwin

2019

Nat Commun

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“…A recent developed open-source structure optimization package StructOpt was designed to enable structure optimization with guidance from multiple experimental data and the potential energy at the same time. 34 The StructOpt package was improved and applied to study the a-TiO 2 system in this work.…”

Section: Introductionmentioning

confidence: 99%