基于机器学习构建的环三亚甲基三硝胺晶体势  

Energetic potential of hexogen constructed by machine learning

作　　者：王鹏举 范俊宇 苏艳[1] 赵纪军[1] Wang Peng-Ju;Fan Jun-Yu;Su Yan;Zhao Ji-Jun(Key Laboratory of Materials Modification by Laser,Ion and Electron Beams,Ministry of Education,Dalian University of Technology,Dalian 116024,China)

机构地区：[1]大连理工大学,三束材料改性教育部重点实验室,大连116024

出　　处：《物理学报》2020年第23期302-310,共9页Acta Physica Sinica

基　　金：国家自然科学基金(批准号:91961204);科学挑战专题(批准号:TZ2016001);中央高校基本科研业务费(批准号:DUT20ZD207)资助的课题.

摘　　要：环三亚甲基三硝胺(RDX)是一种高能低感度炸药,对其能量和性质的准确计算对于开展该炸药的分子模拟至关重要.本文基于机器学习算法,采用高维神经网络模型,对RDX分子晶体结构数据集进行势函数训练.分别采用9种不同的网络结构进行测试训练,并选取其中学习效果最好的势函数对RDX分子晶体结合能和晶格中原子受力进行计算,均能很好地重复出第一性原理的计算结果,其测试集结合能的均方根误差为59.2 meV/atom.作为机器学习势函数的应用,进一步使用该势函数对a相RDX晶体进行分子动力学模拟,以验证其适用性.1,3,5-trinitro-1,3,5-triazacyclohexane(RDX)or hexogen,a high-insensitivity explosive,the accurately description of its energy and properties is of fundamental significance in the sense of security and application.Based on the machine learning method,high-dimensional neural network is used to construct potential function of RDX crystal.In order to acquire enough data in neural network learning,based on the four known crystal phases of RDX,the structural global search is performed under different spatial groups to obtain 15199 structure databases.Here in this study,we use nearby atomic environment to build 72 different basis functions as input neurons,in which the 72 different basis functions represent the interaction with nearby atoms for each type of element.Among them,90%data are randomly set as training set,and the remaining 10%data are taken as test set.To obtain the better training effect,9 different neural network structures carry out 2000 step iterations at most,thereby the 30-30-10 hidden layer structure has the lower root mean square error(RMSE)after the 1847 iterations compared with the energies from first-principles calculations.Thus,the potential function fitted by 30-30-10 hidden layer network is chosen in subsequent calculations.This constructed potential function can reproduce the first-principles results of test set well,with the RMSE of 59.2 meV/atom for binding energy and 7.17 eV/A for atomic force.Especially,the RMSE of the four known RDX crystal phases from 1 atm to 6 GPa are 10.0 meV/atom and 1.11 eV/A for binding energy and atomic force,respectively,indicating that the potential function has a better description of the known structures.Furthermore,we also propose four additional RDX crystal phases with lower enthalpy,which may be alternative crystal phases undetermined in experiment.In addition,based on molecular dynamics simulation with this potential function,the a-phase RDX crystal can stay stable for a few ps,further proving the applicability of our constructed potential function.

关 键 词：含能材料 神经网络 势函数 分子动力学 

分 类 号：TP181[自动化与计算机技术—控制理论与控制工程] TQ560.1[自动化与计算机技术—控制科学与工程]