标题： 基于物理的描述符用于结构多形体的预测 显示英文标题

标题： Physics-guided descriptors for prediction of structural polymorphs

摘要： 我们开发了一种结合机器学习(ML)和密度泛函理论(DFT)的方法，通过引入基于结构畸变模式的物理引导描述符来预测低能多形体。我们系统地生成晶体结构，利用畸变模式并通过单点DFT计算其能量。然后训练一个ML模型，在材料的高维势能面上识别低能构型。在此，我们以BiFeO3作为案例研究，并通过调整一组不同畸变模式的线性组合的振幅来探索其相空间。我们的方法通过重新发现几种已知的具有复杂晶体结构的BiFeO3亚稳相得到了验证，并通过识别21种新的低能多形体证明了其效率。这种方法提出了一种新的途径，以加速固态材料中低能多形体的预测。 显示英文摘要

摘要： 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.