Ab initio quality neural-network potential for sodium
Ab initio quality neural-network potential for sodium
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DOI：
10.1103/physrevb.81.184107
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发表时间：
2010-05-01
影响因子：
3.7
通讯作者：
Parrinello, Michele
中科院分区：
文献类型：
--
作者：
Eshet, Hagai;Khaliullin, Rustam Z.;Parrinello, Michele
利用神经网络（NN）表示从头算势能面，建立了高压高温（HPHT）钠晶体和液相的原子间电位。结果表明，在120 GPa和1200 K的P-T范围内，神经网络电位对液态钠和bcc、fcc和cI16晶体相的多种性质提供了从头开始的质量描述。神经网络电位的计算效率及其在宽P-T范围内重现钠的定量实验特性的能力的独特组合，使HPHT钠的物理化学过程的分子动力学模拟具有前所未有的质量。
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.