Predicting the stability of ternary intermetallics with density functional theory and machine learning

被引：35

作者：

Schmidt, Jonathan ^{[1
]}

Chen, Liming ^{[2
]}

Botti, Silvana ^{[3
,4
]}

Marques, Miguel A. L. ^{[1
]}

机构：

[1] Martin Luther Univ Halle Wittenberg, Inst Phys, D-06099 Halle, Germany

[2] Univ Lyon, Ecole Cent Lyon, Liris Lab, CNRS,UMR 5205, 36 Ave Guy Collongue, F-69134 Ecully, France

[3] Friedrich Schiller Univ Jena, Inst Festkorpertheorie & Opt, Max Wien Pl 1, D-07743 Jena, Germany

[4] European Theoret Spect Facil, Max Wien Pl 1, D-07743 Jena, Germany

来源：

JOURNAL OF CHEMICAL PHYSICS | 2018年 / 148卷 / 24期

关键词：

TOTAL-ENERGY CALCULATIONS; HIGH-THROUGHPUT; CRYSTAL-STRUCTURE; DISCOVERY;

D O I：

10.1063/1.5020223

中图分类号：

O64 [物理化学（理论化学）、化学物理学];

学科分类号：

070304 ; 081704 ;

摘要：

We use a combination of machine learning techniques and high-throughput density-functional theory calculations to explore ternary compounds with the AB(2)C(2) composition. We chose the two most common intermetallic prototypes for this composition, namely, the tI10-CeAl2Ga2 and the tP10-FeMo2B2 structures. Our results suggest that there may be similar to 10 times more stable compounds in these phases than previously known. These are mostly metallic and non-magnetic. While the use of machine learning reduces the overall calculation cost by around 75%, some limitations of its predictive power still exist, in particular, for compounds involving the second-row of the periodic table or magnetic elements. Published by AIP Publishing.

引用

页数：6

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