The XtalOpt Evolutionary Algorithm for Crystal Structure Prediction

Abstract

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.

Tuning Chemical Precompression: Theoretical Design and Crystal Chemistry of Novel Hydrides in the Quest for Warm and Light Superconductivity at Ambient Pressures

“…Whereas the first three are best suited for local exploration, the latter three can broadly traverse the PES. Quantifying the performance of an algorithm is difficult due to their stochastic nature, however it has been shown that various options can greatly enhance the success rate 64 .…”

Structural Diversity and Superconductivity in S-P-H Ternary Hydrides Under Pressure

“…The stoichiometries of the doped phases we considered were S x P 38 In this case the EA searches were seeded with doped supercells from the previously reported Im3m H 3 S and R3m H 3 S structures. 19 The CSP searches were performed using the open-source evolutionary algorithm (EA) XTALOPT [44][45][46] version 12. 47 The initial generation consisted of random symmetric structures that were created by the RANDSPG algorithm, 48 and the minimum interatomic distance between S-S, S-H, S-P, H-H, H-P, and P-P atoms were constrained to 0.88, 0.71, 0.93, 0.53, 0.76 and 0.98 Å using a uniform scaling factor of 0.5 multiplied by tabulated covalent radii.…”

Materials under high pressure: A chemical perspective

“…In the past two decades, a plethora of CSP codes have been developed based on evolutionary (or genetic) algorithms (EAs/GAs), including XtalOpt, [53,78,[149][150][151][152][153][154] GASP, [155] USPEX, [55,156,157] MAISE, [56] EVO, [57], and other codes developed by Trimarchi et al, [58,158], Abraham et al, [59] Fadda et al, [60], Woodley and Catlow [159], Hammer et al [160], and the "adaptive-Ga" of Wentzcovitch et al [68]. These codes are coupled with periodic first-principle simulation packages or interatomic potentials for local optimizations.…”