Abstract：
Modern crystal structure prediction methods based on structure generation algorithms and first-principles calculations play important roles in the design of new materials. However, the cost of these methods is very expensive because their success mostly relies on the efficient sampling of structures and the accurate evaluation of energies for those sampled structures. Herein, we develop a Machine-learning-Assisted CRYStalline Materials sAmpling sysTem (MAXMAT) aiming to accelerate the prediction of new crystal structures. For a given chemical composition, MAXMAT can generate efficient crystal structures with the help of a Python package for crystal structure generation (PyXtal) and can quickly evaluate the energies of these generated structures using a well-developed machine learning interaction potential model (M3GNET). We have used MAXMAT to perform crystal structure searches for three different chemical systems (TiO2, MgAl2O4, and BaBOF3) to test its accuracy and efficiency. Furthermore, we apply MAXMAT to predict new nonlinear optical materials, suggesting several thermodynamically synthesizable structures with high performance in LiZnGaS3 and CaBOF3 systems.
摘要：
基于结构生成算法和第一性原理计算的现代晶体结构预测方法在新材料的设计中起着重要作用。然而,这些方法的成本非常昂贵，因为它们的成功主要依赖于结构的有效采样和对这些采样结构的能量的准确评估。在这里，我们开发了一种机器学习辅助的晶体材料放大系统（MAXMAT），旨在加速新晶体结构的预测。对于给定的化学成分，MAXMAT可以在用于晶体结构生成的Python封装（PyXtal）的帮助下生成有效的晶体结构，并可以使用完善的机器学习交互潜力模型（M3GNET）快速评估这些生成结构的能量。我们已经使用MAXMAT对三种不同的化学系统（TiO2，MgAl2O4和BaBOF3）进行了晶体结构搜索，以测试其准确性和效率。此外，我们应用MAXMAT预测新的非线性光学材料，表明在LiZnGaS3和CaBOF3系统中具有高性能的几种热力学可合成结构。
