On-the-Fly Machine Learning of Atomic Potential in Density Functional Theory Structure Optimization

被引：112

作者：

Jacobsen, T. L.

Jorgensen, M. S.

Hammer, B. ^{[1
]}

机构：

[1] Aarhus Univ, Dept Phys & Astron, DK-8000 Aarhus C, Denmark

来源：

PHYSICAL REVIEW LETTERS | 2018年 / 120卷 / 02期

关键词：

LOWEST-ENERGY STRUCTURES; EVOLUTIONARY ALGORITHMS; GEOMETRY OPTIMIZATION; GENETIC ALGORITHMS; GLOBAL MINIMUM; SURFACE; CLUSTERS; SEARCH;

D O I：

10.1103/PhysRevLett.120.026102

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

Machine learning (ML) is used to derive local stability information for density functional theory calculations of systems in relation to the recently discovered SnO2(110)-(4x1) reconstruction. The ML model is trained on (structure, total energy) relations collected during global minimum energy search runs with an evolutionary algorithm (EA). While being built, the ML model is used to guide the EA, thereby speeding up the overall rate by which the EA succeeds. Inspection of the local atomic potentials emerging from the model further shows chemically intuitive patterns.

引用

页数：5

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