论文标题
物理指导的描述符，用于预测结构多晶型物
Physics-guided descriptors for prediction of structural polymorphs
论文作者
论文摘要
我们开发了一种结合机器学习（ML）和密度功能理论（DFT）的方法，以通过基于结构变形模式引入物理引导的描述符来预测低能多晶型物。我们使用失真模式系统地生成晶体结构，并通过单点DFT计算计算其能量。然后，我们训练ML模型，以鉴定材料高维势能表面上的低能构型。在这里，我们使用BifeO3作为案例研究，并通过调整有限的一组不同失真模式的线性组合幅度来探索其相空间。通过重新发现具有复杂晶体结构的BifeO3的几个已知亚稳态阶段来验证我们的过程，并通过识别21种新的低能多晶型物来证明其效率。这种方法提出了一种新的途径，以加速固态材料中低能多晶型物的预测。
We develop a method combining machine learning (ML) and density functional theory (DFT) to predict low-energy polymorphs by introducing physics-guided descriptors based on structural distortion modes. We systematically generate crystal structures utilizing the distortion modes and compute their energies with single-point DFT calculations. We then train a ML model to identify low-energy configurations on the material's high-dimensional potential energy surface. Here, we use BiFeO3 as a case study and explore its phase space by tuning the amplitudes of linear combinations of a finite set of distinct distortion modes. Our procedure is validated by rediscovering several known metastable phases of BiFeO3 with complex crystal structures, and its efficiency is proved by identifying 21 new low-energy polymorphs. This approach proposes a new avenue toward accelerating the prediction of low-energy polymorphs in solid-state materials.
