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  • ISSN 1006-3080
  • CN 31-1691/TQ

基于机器学习的碳酸盐微观结构与性质

杨博 路贵民

杨博, 路贵民. 基于机器学习的碳酸盐微观结构与性质[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cnki.1006-3080.20210331004
引用本文: 杨博, 路贵民. 基于机器学习的碳酸盐微观结构与性质[J]. 华东理工大学学报(自然科学版). doi: 10.14135/j.cnki.1006-3080.20210331004
YANG Bo, LU Guimin. Microstructure and Properties of Carbonate Salt Based on Machine Learning[J]. Journal of East China University of Science and Technology. doi: 10.14135/j.cnki.1006-3080.20210331004
Citation: YANG Bo, LU Guimin. Microstructure and Properties of Carbonate Salt Based on Machine Learning[J]. Journal of East China University of Science and Technology. doi: 10.14135/j.cnki.1006-3080.20210331004

基于机器学习的碳酸盐微观结构与性质

doi: 10.14135/j.cnki.1006-3080.20210331004
基金项目: 国家自然科学基金(U20A20147)
详细信息
    作者简介:

    杨博:杨 博(1996—),男,甘肃人,硕士生,主要从事碳酸盐的相关模拟计算。E-mail:b0yang@foxmail.com

    通讯作者:

    路贵民,E-mail:gmlu@ecust.edu.cn

  • 中图分类号: O552

Microstructure and Properties of Carbonate Salt Based on Machine Learning

  • 摘要: 采用第一性原理计算、机器学习以及经典分子动力学模拟联用的方法对K2CO3和Na2CO3熔融状态下的结构和性质进行计算,计算结果表明,K2CO3的能量(单位原子能量之和,余同)与受力的均方根误差分别为8.62×10−4 eV和4.67×108 eV/m;Na2CO3的能量与受力的均方根误差分别为1.19×10−3 eV和5.31×108 eV/m,计算值与文献值吻合较好。K2CO3的密度、比热容和热导率的计算偏差分别约为5.0%、3.3%和8.0%,Na2CO3的密度、比热容和热导率的计算偏差分别约为5.6%、6.0%和3.5%。

     

  • 图  1  学习过程中K2CO3能量和受力的均方根误差

    Figure  1.  EMSEs of energy and force of K2CO3 during learning process

    图  2  K2CO3机器学习的预测结果与DFT计算结果的对比

    Figure  2.  Comparsion of ML prediction result and DFT calculation result of K2CO3

    图  3  学习过程中Na2CO3能量和受力的均方根误差

    Figure  3.  RMSEs of energy and force of Na2CO3 during learning process

    图  4  Na2CO3机器学习的预测结果与DFT计算结果的对比

    Figure  4.  Comparsion of ML prediction result and DFT calculation result of Na2CO3

    图  5  不同温度下K2CO3径向分布函数的DPMD与DFT计算结果对比

    Figure  5.  Comparsion of radial distribution functions of K2CO3 derived from DPMD and DFT calculation at different temperatures

    图  6  DPMD方法计算所得的K2CO3径向分布函数

    Figure  6.  Radial distribution functions of K2CO3 calculated by DPMD method

    图  7  不同温度下Na2CO3径向分布函数的DPMD与DFT计算结果对比

    Figure  7.  Comparsion of radial distribution functions of Na2CO3 derived from DPMD and DFT calculation at different temperatures

    图  8  DPMD方法计算所得的Na2CO3径向分布函数

    Figure  8.  Radial distribution functions of Na2CO3 calculated by DPMD method

    图  9  不同温度下K2CO3和Na2CO3的密度文献值与模拟值对比

    Figure  9.  Comparison of literature and simulated density of K2CO3 and Na2CO3 at different temperatures

    图  10  K2CO3和Na2CO3计算中焓随温度的变化

    Figure  10.  Changes of enthalpy of K2CO3 and Na2CO3 with temperature

    图  11  K2CO3和Na2CO3热导率的文献值与模拟值对比

    Figure  11.  Comparison of literature and simulated thermal conductivity of K2CO3 and Na2CO3

    图  12  K2CO3和Na2CO3归一化张量自相关函数

    Figure  12.  Normalized stress tensor auto-correlation function of K2CO3 and Na2CO3

    图  13  K2CO3和Na2CO3黏度的文献值与模拟值对比

    Figure  13.  Comparison of literature and simulated viscosity of K2CO3 and Na2CO3

    表  1  K2CO3和Na2CO3的比热容的模拟值与文献值对比

    Table  1.   Comparison of simulated and literature values of the Cp of K2CO3 and Na2CO3

    Carbonate salt Cp/(J·g−1·K−1)
    Simulated valueLiterature value
    K2CO31.561.51
    Na2CO31.721.83
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出版历程
  • 收稿日期:  2021-03-31
  • 网络出版日期:  2021-07-02

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