Exploration of crystal structure and the origin of unexpected intrinsic ductility of χCo7Ta2

Xu, Weiwei; Yu, Qinxue; Xia, Fangfang; Yin, Guohui; Huang, Yuelong; Chen, Lijie

doi:10.1016/j.jallcom.2019.05.223

Cited by 5 publications

(1 citation statement)

References 42 publications

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“…According to these parameter settings, the total number of local relaxations is up to 2 × 10 7 . In the following, we consider three prerequisites that should be satisfied for a stable crystal structure: (i) thermodynamic stability, which is estimated by the formation energy and convex hull; (ii) dynamic stability, which can be assessed by phonon dispersions; and (iii) mechanical stability, which is evaluated via elastic constants (Xu et al, 2019 ).…”

Section: Resultsmentioning

confidence: 99%

Crystal Structure Prediction of Binary Alloys via Deep Potential

Wang

Zhang

et al. 2020

Front. Chem.

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Predicting crystal structure has been a challenging problem in physics and materials science for a long time. A reliable energy calculation engine combined with an efficient global search algorithm, such as particle swarm optimization algorithm or genetic algorithm, is needed to conduct crystal structure prediction. In recent years, machine learning-based interatomic potential energy surface models have been proposed, potentially allowing us to perform crystal structure prediction for systems with the accuracy of density functional theory (DFT) and the speed of empirical force fields. In this paper, we employ a previously developed Deep Potential model to predict the intermetallic compound of the aluminum–magnesium system, and find six meta-stable phases with negative or nearly zero formation energy. In particular, Mg12Al8 shows excellent ductility and Mg5Al27 has a high Young's modulus. Based on our benchmark results, we propose a relatively robust structure screening criterion that selects potentially stable structures from the Deep Potential-based convex hull and performs DFT refinement. By using this criterion, the computational cost needed to construct the convex hull with ab initio accuracy can be dramatically reduced.

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Section: Resultsmentioning

confidence: 99%