Machine learning the density functional theory potential energy surface for the inorganic halide perovskite CsPbBr3
Machine learning the density functional theory potential energy surface for the inorganic halide perovskite CsPbBr3
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DOI：
10.1103/physrevb.100.134101
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发表时间：
2019-07
影响因子：
3.7
通讯作者：
John C. Thomas;J. S. Bechtel;A. Natarajan;A. Van der Ven
中科院分区：
文献类型：
--
作者：
John C. Thomas;J. S. Bechtel;A. Natarajan;A. Van der Ven
作为温度函数的结构相变决定了许多技术上重要的材料的结构-功能关系。谐波哈密顿量已被证明在预测许多材料的振动特性方面是成功的。然而，他们是不够的模拟结构相变的晶体与势能面，要么是强烈的非谐或非\n凸的集体原子位移或均匀应变。在本文中，我们开发了一个框架来表达高度非谐的第一性原理势能面作为集体集团变形的多项式。我们进一步适应的方法，通过使用人工神经网络模型的集群扩展形式主义的非线性扩展。机器学习模型在第一原理计算的大型数据库上进行训练，并显示出以低误差再现势能表面。
Structural phase transitions as a function of temperature dictate the structure--functionality relationships in many technologically important materials. Harmonic Hamiltonians have proven successful in predicting the vibrational properties of many materials. However, they are inadequate for modeling structural phase transitions in crystals with potential energy surfaces that are either strongly anharmonic or no\ n-convex with respect to collective atomic displacements or homogeneous strains. In this paper we develop a framework to express highly anharmonic first-principles potential energy surfaces as polynomials of collective cluster deformati\ ons. We further adapt the approach to a nonlinear extension of the cluster expansion formalism through the use of an artificial neural net model. The machine learning models are trained on a large database of first-principles calculations and are shown to reproduce the potential energy surface with l\ ow error.