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  • 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. Research on Microstructure and Physical Properties of Molten 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. Research on Microstructure and Physical Properties of Molten 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(1996—),男,甘肃人,硕士研究生,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

Research on Microstructure and Physical Properties of Molten 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能量与受力的测试误差分别为8.62×10-4eV/atom和4.67×10-2eV/10-10m;Na2CO3测试误差为1.19×10-3eV/atom和5.31×10-2eV/10-10m。结果表明计算值与实验值吻合较好,K2CO3密度、比热和热导率的计算偏差分别为5.0%、3.3%和8.0%,Na2CO3密度、比热和热导率的计算偏差分别为5.6%、6.5%和3.5%。

     

  • 图  1  K2CO3学习过程中能量和受力的均方根误差 ((a)能量,(b)受力)

    Figure  1.  The energy and force RMSEs of K2CO3 during learning process ((a)energy, (b)force)

    图  2  K2CO3机器学习的预测结果与DFT计算值的对比((a)能量, (b-d)受力)

    Figure  2.  Comparsion of ML prediction data and DFT calculation of K2CO3 ((a)energy, (b-d)force)

    图  3  Na2CO3学习过程中能量和受力的均方根误差 ((a)能量,(b)受力)

    Figure  3.  The energy and force RMSEs of Na2CO3 during learning process ((a)energy, (b)force)

    图  4  Na2CO3机器学习的预测结果与DFT计算值的对比((a)能量, (b-d)受力)

    Figure  4.  Comparsion of ML prediction data and DFT calculation of Na2CO3 ((a)energy, (b-d)force)

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

    Figure  5.  Comparsion of Radial distribution functions of K2CO3 derived from DPMD and FPMD simulation at different temperature

    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FPMD计算结果对比

    Figure  7.  Comparsion of Radial distribution functions of Na2CO3 derived from DPMD and FPMD simulation at different temperature

    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的密度实验值与模拟值对比

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

    图  10  (a)K2CO3计算中焓随温度的变化,(b) Na2CO3计算中焓随温度的变化

    Figure  10.  Changes of enthalpy of (a) K2CO3, (b) Na2CO3 with temperature

    图  11  K2CO3和Na2CO3的热导率实验值与模拟值的对比

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

    图  12  归一化张量自相关函数((a) K2CO3 (b) Na2CO3)

    Figure  12.  Normalized stress tensor time auto-correlation function ((a) K2CO3 (b) Na2CO3)

    图  13  K2CO3和Na2CO3的黏度实验值与计算值的对比

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

    表  1  K2CO3和Na 2CO3的比热的模拟值与实验值对比

    Table  1.   Comparison of experimental and simulated results of the Cp of K2CO3 and Na 2CO3

    Cp/(J·g-1·K-1)K2CO3Na 2CO3
    计算值1.561.72
    实验值1.511.83
    下载: 导出CSV
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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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