Predicting Thermal Properties of Crystals Using Machine Learning
Predicting Thermal Properties of Crystals Using Machine Learning
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DOI：
10.1002/adts.201900208
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发表时间：
2019-12-17
影响因子：
3.3
通讯作者：
Winkler, David A.
中科院分区：
文献类型：
--
作者：
Tawfik, Sherif Abdulkader;Isayev, Olexandr;Winkler, David A.
用量子力学方法计算晶体的振动性质是计算材料科学中的一个具有挑战性的问题。这个问题可以使用互补的机器学习方法来解决，这些方法可以快速可靠地概括熵，比热，有效的多晶介电函数以及通过精确但冗长的QM方法计算的材料的非振动特性（带隙）。使用属性标记的材料片段描述符数学描述的材料。机器学习模型预测的QM属性的均方根误差为0.31 meV每原子每K的熵，0.18 meV每原子每K的比热，4.41的痕迹的介电张量，和0.5 eV的带隙。这些模型足够精确，可以快速筛选大量的晶体结构，以加速材料的发现。
Calculating vibrational properties of crystals using quantum mechanical (QM) methods is a challenging problem in computational material science. This problem is solved using complementary machine learning methods that rapidly and reliably recapitulate entropy, specific heat, effective polycrystalline dielectric function, and a non-vibrational property (band gap) for materials calculated by accurate but lengthy QM methods. The materials are described mathematically using property-labeled materials fragment descriptors. The machine learning models predict the QM properties with root mean square errors of 0.31 meV per atom per K for entropy, 0.18 meV per atom per K for specific heat, 4.41 for the trace of the dielectric tensor, and 0.5 eV for band gap. These models are sufficiently accurate to allow rapid screening of large numbers of crystal structures to accelerate material discovery.