Defects dictate the properties of many functional materials. To understand the behaviour of defects and their impact on physical properties, it is necessary to identify the most stable defect geometries. However, global structure searching is computationally challenging for high-throughput defect studies or materials with complex defect landscapes, like alloys or disordered solids. Here, we tackle this limitation by harnessing a machine-learning surrogate model to qualitatively explore the structural landscape of neutral point defects. By learning defect motifs in a family of related metal chalcogenide and mixed anion crystals, the model successfully predicts favourable reconstructions for unseen defects in unseen compositions for 90% of cases, thereby reducing the number of first-principles calculations by 73%. Using CdSe_xTe_1−x alloys as an exemplar, we train a model on the end member compositions and apply it to find the stable geometries of all inequivalent vacancies for a range of mixing concentrations, thus enabling more accurate and faster defect studies for configurationally complex systems.

缺陷决定了许多功能材料的特性。为了了解缺陷的行为及其对物理性能的影响，有必要确定最稳定的缺陷几何形状。然而，对于高通量缺陷研究或具有复杂缺陷景观的材料（如合金或无序固体）来说，全局结构搜索在计算上具有挑战性。在这里，我们通过利用机器学习替代模型来定性探索中性点缺陷的结构景观来解决这一限制。通过学习一系列相关金属硫族化物和混合阴离子晶体中的缺陷基序，该模型成功预测了 90% 情况下未见成分中未见缺陷的有利重建，从而将第一性原理计算的数量减少了 73%。使用 CdSe _x Te _1−x 合金作为范例，我们训练了端元成分模型，并应用它来找到一系列混合浓度下所有不等价空位的稳定几何形状，从而能够对配置复杂的系统进行更准确、更快速的缺陷研究。