Ab initio quality neural-network potential for sodium

被引：114

作者：

Eshet, Hagai ^{[1
]}

Khaliullin, Rustam Z. ^{[1
]}

Kuehne, Thomas D. ^{[2
,3
,4
]}

Behler, Joerg ^{[5
]}

Parrinello, Michele ^{[1
]}

机构：

[1] Swiss Fed Inst Technol, Dept Chem & Appl Biosci, CH-6900 Lugano, Switzerland

[2] Inst Phys Chem, D-55128 Mainz, Germany

[3] Ctr Computat Sci, D-55128 Mainz, Germany

[4] Johannes Gutenberg Univ Mainz, D-55128 Mainz, Germany

[5] Ruhr Univ Bochum, Lehrstuhl Theoret Chem, D-44780 Bochum, Germany

来源：

PHYSICAL REVIEW B | 2010年 / 81卷 / 18期

关键词：

EMBEDDED-ATOM METHOD; MOLECULAR-DYNAMICS SIMULATION; ELASTIC-CONSTANTS; ENERGY SURFACES; 1ST PRINCIPLES; LIQUID-SODIUM; CUBIC METALS; ALKALI-METALS; BCC METALS; PRESSURE;

D O I：

10.1103/PhysRevB.81.184107

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

An interatomic potential for high-pressure high-temperature (HPHT) crystalline and liquid phases of sodium is created using a neural-network (NN) representation of the ab initio potential-energy surface. It is demonstrated that the NN potential provides an ab initio quality description of multiple properties of liquid sodium and bcc, fcc, and cI16 crystal phases in the P-T region up to 120 GPa and 1200 K. The unique combination of computational efficiency of the NN potential and its ability to reproduce quantitatively experimental properties of sodium in the wide P-T range enables molecular-dynamics simulations of physicochemical processes in HPHT sodium of unprecedented quality.

引用

页数：8

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