论文标题
通过组合图网络和贝叶斯优化的晶体结构预测
Crystal structure prediction via combining graph network and Bayesian optimization
论文作者
论文摘要
我们通过梳理网络（GN）和贝叶斯优化（BO）开发了一种密度无功能理论的方法来预测晶体结构。 GN被采用以建立晶体结构和形成焓之间的相关模型。 BO将以最佳的形成焓加速搜索晶体结构。将GN和BO组合进行晶体结构搜索（GN-BOSS）的方法原则上可以预测给定的化学成分处的晶体结构，而不会对细胞形状和晶格对称性进行其他约束。然后通过解决经典的ph-vv挑战来验证GN-Boss方法的适用性和效率。它可以从划痕中正确预测24种二元化合物的晶体结构，平均计算成本约为30分钟，仅一个CPU核心。 GN-BOSS方法可能在不使用昂贵的DFT计算的情况下为数据驱动的晶体结构预测打开了新的途径。
We developed a density functional theory-free approach for crystal structure prediction via combing graph network (GN) and Bayesian optimization (BO). GN is adopted to establish the correlation model between crystal structure and formation enthalpies. BO is to accelerate searching crystal structure with optimal formation enthalpy. The approach of combining GN and BO for crystal Structure Searching (GN-BOSS), in principle, can predict crystal structure at given chemical compositions without additional constraints on cell shapes and lattice symmetries. The applicability and efficiency of GN-BOSS approach is then verified via solving the classical Ph-vV challenge. It can correctly predict the crystal structures of 24 binary compounds from scratch with averaged computational cost ~ 30 minutes each by only one CPU core. GN-BOSS approach may open a new avenue to data-driven crystal structural prediction without using the expensive DFT calculations.
