Bai Qiang, Duan Yunrui, Lian Jie, Wang Xiaomin
College of Materials Science and Engineering, Taiyuan University of Technology, Taiyuan, China.
Front Chem. 2022 Aug 26;10:964953. doi: 10.3389/fchem.2022.964953. eCollection 2022.
The emerging KNiF-type oxyhydrides with unique hydride ions (H) and O coexisting in the anion sublattice offer superior functionalities for numerous applications. However, the exploration and innovations of the oxyhydrides are challenged by their rarity as a limited number of compounds reported in experiments, owing to the stringent laboratory conditions. Herein, we employed a suite of computations involving ab initio methods, informatics and machine learning to investigate the stability relationship of the KNiF-type oxyhydrides. The comprehensive stability map of the oxyhydrides chemical space was constructed to identify 76 new compounds with good thermodynamic stabilities using the high-throughput computations. Based on the established database, we reveal geometric constraints and electronegativities of cationic elements as significant factors governing the oxyhydrides stabilities via informatics tools. Besides fixed stoichiometry compounds, mixed-cation oxyhydrides can provide promising properties due to the enhancement of compositional tunability. However, the exploration of the mixed compounds is hindered by their huge quantity and the rarity of stable oxyhydrides. Therefore, we propose a two-step machine learning workflow consisting of a simple transfer learning to discover 114 formable oxyhydrides from thousands of unknown mixed compositions. The predicted high H conductivities of the representative oxyhydrides indicate their suitability as energy conversion materials. Our study provides an insight into the oxyhydrides chemistry which is applicable to other mixed-anion systems, and demonstrates an efficient computational paradigm for other materials design applications, which are challenged by the unavailable and highly unbalanced materials database.
新兴的KNiF型羟基氢化物在阴离子亚晶格中具有独特的氢负离子（H⁻）和O共存，为众多应用提供了卓越的功能。然而，由于实验中报道的化合物数量有限，且实验室条件苛刻，羟基氢化物的探索和创新面临着挑战。在此，我们采用了一系列包括从头算方法、信息学和机器学习的计算方法，来研究KNiF型羟基氢化物的稳定性关系。通过高通量计算构建了羟基氢化物化学空间的综合稳定性图，以识别76种具有良好热力学稳定性的新化合物。基于已建立的数据库，我们通过信息学工具揭示了阳离子元素的几何约束和电负性是控制羟基氢化物稳定性的重要因素。除了固定化学计量比的化合物外，混合阳离子羟基氢化物由于组成可调性的增强，可以提供有前景的性能。然而，混合化合物的探索受到其数量巨大和稳定羟基氢化物稀少的阻碍。因此，我们提出了一个两步机器学习工作流程，包括一个简单的迁移学习，从数千种未知的混合组成中发现114种可形成的羟基氢化物。代表性羟基氢化物预测的高氢电导率表明它们适合作为能量转换材料。我们的研究为适用于其他混合阴离子系统的羟基氢化物化学提供了见解，并展示了一种适用于其他材料设计应用的高效计算范式，这些应用受到不可用和高度不平衡的材料数据库的挑战。
