Materials with high-dielectric constant easily polarize under external electric fields, allowing them to perform essential functions in many modern electronic devices. Their practical utility is determined by two conflicting properties: high dielectric constants tend to occur in materials with narrow band gaps, limiting the operating voltage before dielectric breakdown. We present a high-throughput workflow that combines element substitution, ML pre-screening, ab initio simulation and human expert intuition to efficiently explore the vast space of unknown materials for potential dielectrics, leading to the synthesis and characterization of two novel dielectric materials, CsTaTeO6 and Bi2Zr2O7. Our key idea is to deploy ML in a multi-objective optimization setting with concave Pareto front. While usually considered more challenging than single-objective optimization, we argue and show preliminary evidence that the $1/x$-correlation between band gap and permittivity in fact makes the task more amenable to ML methods by allowing separate models for band gap and permittivity to each operate in regions of good training support while still predicting materials of exceptional merit. To our knowledge, this is the first instance of successful ML-guided multi-objective materials optimization achieving experimental synthesis and characterization. CsTaTeO6 is a structure generated via element substitution not present in our reference data sources, thus exemplifying successful de-novo materials design. Meanwhile, we report the first high-purity synthesis and dielectric characterization of Bi2Zr2O7 with a band gap of 2.27 eV and a permittivity of 20.5, meeting all target metrics of our multi-objective search.

翻译：高介电常数材料在外加电场作用下容易极化，因而在现代众多电子器件中发挥关键作用。其实际效用由两个相互矛盾的性质决定：高介电常数往往出现在窄带隙材料中，这限制了介电击穿前的额定工作电压。我们提出了一种高通量工作流程，结合元素替换、机器学习预筛选、第一性原理模拟和人类专家直觉，以高效探索未知材料的广阔空间来寻找潜在介电体，最终成功合成并表征了两种新型介电材料：CsTaTeO6和Bi2Zr2O7。我们的核心思想是在具有凹形帕累托前沿的多目标优化框架中部署机器学习。尽管这通常被认为比单目标优化更具挑战性，但我们论证并提供了初步证据表明，带隙与介电常数之间的$1/x$相关性实际上使该任务更适于机器学习方法：通过分别为带隙和介电常数建立独立模型，每个模型可在训练数据充分支持的区域内运行，同时仍能预测具有卓越性能的材料。据我们所知，这是首次实现由机器学习引导、并完成实验合成与表征的多目标材料优化成功案例。CsTaTeO6是通过元素替换生成的结构，并未出现在我们的参考数据源中，因此成功展示了从零开始（de-novo）的材料设计。同时，我们报告了Bi2Zr2O7的首次高纯度合成及其介电表征，其带隙为2.27 eV，介电常数为20.5，满足了我们多目标搜索的所有目标指标。