Large-scale machine-learning-assisted exploration of the whole materials space

Crystal-graph attention networks have emerged recently as remarkable tools for the prediction of thermodynamic stability and materials properties from unrelaxed crystal structures. Previous networks trained on two million materials exhibited, however, strong biases originating from underrepresented chemical elements and structural prototypes in the available data. We tackled this issue computing additional data to provide better balance across both chemical and crystal-symmetry space. Crystal-graph networks trained with this new data show unprecedented generalization accuracy, and allow for reliable, accelerated exploration of the whole space of inorganic compounds. We applied this universal network to performed machine-learning assisted high-throughput materials searches including 2500 binary and ternary prototypes and spanning about 1 billion compounds. After validation using density-functional theory, we uncover in total 19512 additional materials on the convex hull of thermodynamic stability and around 150000 compounds with a distance of less than 50 meV/atom from the hull. Here we include the DCGAT-1, DCGAT-2, and DCGAT-3 datasets used in this work.

近年来，晶体图注意力网络（Crystal-graph Attention Networks）已成为从非弛豫晶体结构预测热力学稳定性与材料物性的前沿工具。然而，此前基于200万种材料训练得到的模型存在显著偏差，其根源在于可用数据中化学元素与结构原型的样本占比不均。为解决这一问题，我们通过补充计算数据，实现了化学空间与晶体对称空间内的数据分布均衡。使用该新增数据集训练得到的晶体图网络展现出前所未有的泛化精度，可实现对无机化合物全空间的可靠且高效探索。我们将该通用网络应用于机器学习辅助的高通量材料筛选，该筛选涵盖2500种二元及三元结构原型，涉及约10亿种化合物。经密度泛函理论（Density-Functional Theory, DFT）验证后，我们共发现19512种位于热力学稳定性凸包上的新增材料，以及约15万种与凸包距离小于50 meV/atom的化合物。本文附带了本研究中使用的DCGAT-1、DCGAT-2及DCGAT-3数据集。