铀、锆以及铀锆合金精确原子间势的深度学习  

Deep Learning of Accurate Interatomic Potentials for Uranium,Zirconium and Uranium-Zirconium Alloy

作　　者：尹晚秋 薄涛 赵玉宝[1] 张蕾 柴之芳 石伟群[3] YIN Wan-qiu;BO Tao;ZHAO Yu-bao;ZHANG Lei;CHAI Zhi-fang;SHI Wei-qun(School of Chemistry and Chemical Engineering,University of South China,Hengyang 421001,China;Engineering Laboratory of Advanced Energy Materials,Ningbo Institute of Materials Technology and Engineering,Chinese Academy of Sciences,Ningbo 315201,China;Laboratory of Nuclear Energy Chemistry,Institute of High Energy Physics,Chinese Academy of Sciences,Beijing 100049,China)

机构地区：[1]南华大学化学化工学院,湖南衡阳421001 [2]中国科学院宁波材料技术与工程研究所先进能源材料工程实验室,浙江宁波315201 [3]中国科学院高能物理研究所核能放射化学实验室,北京100049

出　　处：《核化学与放射化学》2024年第5期450-461,I0002,共13页Journal of Nuclear and Radiochemistry

基　　金：国家自然科学基金资助项目(12105196)。

摘　　要：铀锆合金作为一体化快堆的重要核燃料,使用先进的计算方法研究其高温下的基础物理性质意义重大。使用深度势能分子动力学方法分别计算了体心立方铀、锆和铀锆合金材料的基础物理性质,该方法兼具第一性原理的高精度和经典分子动力学的高效率。首先,通过使用深度神经网络机器学习训练了体心立方锆(Zr-BCC)、体心立方铀(U-BCC)和体心立方铀锆合金(U-Zr(BCC))的深度势能(DP)模型,其预测的平衡状态方程、晶格常数、弹性性质和声子谱能够达到第一性原理的精度。接着,使用DP模型预测了Zr-BCC、UBCC和U-Zr(BCC)的恒压热容和密度随温度的变化,并且结果能够很好地与实验值吻合。该研究结果表明机器学习方法为成功探索更复杂的核燃料性质提供了重要的路径。Uranium-zirconium alloy is an important nuclear fuel in Integral Fast Reactor,which is of great significance to study its basic physical properties at high temperature by using advanced calculation methods.This work used deep potential molecular dynamics method,which combines the high accuracy of the first principles with the high efficiency of the classical molecular dynamics,to perform an evaluation of the static and thermophysical properties of body-centered cubic phase zirconium,uranium,and uraniumzirconium alloy.Firstly,the deep potential(DP)models of body-centered cubic zirconium(Zr-BCC),bodycentered cubic uranium(U-BCC),and body-centered cubic uranium-zirconium alloy(U-Zr(BCC))were trained by using deep neural network machine learning.Secondly,the DP models were used to predict equilibrium state equation,lattice constant,elastic properties,and phonon spectrum of the three systems,and the predicted results can reach the accuracy of the first principles.Then,the variation of heat capacity and density at constant pressure of Zr-BCC,U-BCC,and U-Zr(BCC)with temperature were predicted by using DP models,and the results are in good agreement with the experimental values.The research results show that the machine learning method provides an important path for successfully exploring more complex nuclear fuel properties.

关 键 词：铀锆合金 机器学习 第一性原理 分子动力学 热力学性质 

分 类 号：TL214.5[核科学技术—核燃料循环与材料] O469[理学—凝聚态物理]