The discovery of high-dielectric materials is crucial to increasing the efficiency of electronic devices and batteries. Here, we report three previously unexplored materials with very high dielectric constants (69 < ϵ < 101) and large band gaps (2.9 < E_g(eV) < 5.5) obtained by screening materials databases using statistical optimization algorithms aided by artificial neural networks (ANN). Two of these new dielectrics are mixed-anion compounds (Eu₅SiCl₆O₄ and HoClO) and are shown to be thermodynamically stable against common semiconductors via phase diagram analysis. We also uncovered four other materials with relatively large dielectric constants (20 < ϵ < 40) and band gaps (2.3 < E_g(eV) < 2.7). While the ANN training-data are obtained from the Materials Project, the search-space consists of materials from the Open Quantum Materials Database (OQMD)—demonstrating a successful implementation of cross-database materials design. Overall, we report the dielectric properties of 17 materials calculated using ab initio calculations, that were selected in our design workflow. The dielectric materials with high-dielectric properties predicted in this work open up further experimental research opportunities.

高介电材料的发现对于提高电子设备和电池的效率至关重要。在这里，我们报告了三种以前未开发的材料，它们具有非常高的介电常数 (69 <  ϵ  < 101) 和大带隙 (2.9 <  E _g (eV) < 5.5)，这些材料是通过使用人工神经网络辅助的统计优化算法筛选材料数据库获得的。安）。其中两种新型电介质是混合阴离子化合物（Eu ₅ SiCl ₆ O ₄和 HoClO），并且通过相图分析显示对普通半导体具有热力学稳定性。我们还发现了其他四种具有相对较大介电常数的材料 (20 <  ϵ < 40) 和带隙 (2.3 <  E _g (eV) < 2.7)。虽然 ANN 训练数据是从材料项目中获得的，但搜索空间由来自开放量子材料数据库 (OQMD) 的材料组成——展示了跨数据库材料设计的成功实施。总体而言，我们报告了使用从头计算计算的 17 种材料的介电特性，这些材料是在我们的设计工作流程中选择的。在这项工作中预测的具有高介电性能的介电材料开辟了进一步的实验研究机会。