Microstructure and Properties of Carbonate Salt Based on Machine Learning
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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%。Abstract: Molten alkali carbonate are widely concerned as potential thermal storage and transfer materials in solar power utilization. As an effective method in molten salt research, computer simulations have been widely used. Local structure and some physical properties of K2CO3 and Na2CO3 at different temperatures were calculated by a complex simulation method including first-principle molecular dynamics, machine learning and classical molecular dynamics. In this method, first-principle molecular dynamics offered accuracy structure information, machine learning was used to create deep potential from structure information to describe potential energy of system, and classical molecular dynamics was used to perform large scale simulation. This complex method could reduce calculation errors caused by potential functions and empirical parameters. The calculation results showed that energy and force test errors of K2CO3 were 8.62×10−4 eV and 4.67×108 eV/m, respectively; energy and force test errors of Na2CO3 were 1.19×10−3 eV and 5.31×108 eV/m, respectively. In all simulation processes, the carbonate ion was a standard equilateral triangle structure in the system, and carbonate clusters were slightly loosened with the increase of temperature; the distance between anions and cations gradually increased with the increase of temperature. The result showed that simulated data of the property was in good agreement with the literature value. The deviations of density, specific heat capacity and thermal conductivity of K2CO3 were about 5.0%, 3.3% and 8.0%. The deviations of density, specific heat capacity and thermal conductivity of Na2CO3 were about 5.6%, 6.0% and 3.5%.
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Key words:
- ionic structure /
- properties of molten salt /
- molecular dynamics /
- molten carbonate salt
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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 value Literature value K2CO3 1.56 1.51 Na2CO3 1.72 1.83 
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