Many technological applications depend on the response of materials to electric fields, but available databases of such responses are limited. Here, we explore the infrared, piezoelectric, and dielectric properties of inorganic materials by combining high-throughput density functional perturbation theory and machine learning approaches. We compute Γ-point phonons, infrared intensities, Born-effective charges, piezoelectric, and dielectric tensors for 5015 non-metallic materials in the JARVIS-DFT database. We find 3230 and 1943 materials with at least one far and mid-infrared mode, respectively. We identify 577 high-piezoelectric materials, using a threshold of 0.5 C/m². Using a threshold of 20, we find 593 potential high-dielectric materials. Importantly, we analyze the chemistry, symmetry, dimensionality, and geometry of the materials to find features that help explain variations in our datasets. Finally, we develop high-accuracy regression models for the highest infrared frequency and maximum Born-effective charges, and classification models for maximum piezoelectric and average dielectric tensors to accelerate discovery.

许多技术应用取决于材料对电场的响应，但是此类响应的可用数据库有限。在这里，我们通过结合高通量密度泛函微扰理论和机器学习方法来探索无机材料的红外，压电和介电性能。我们在JARVIS-DFT数据库中计算了5015种非金属材料的Γ点声子，红外强度，玻恩有效电荷，压电和介电张量。我们发现分别具有至少一种远红外和中红外模式的3230和1943材料。我们使用0.5 C / m ²的阈值确定了577种高压电材料。使用20的阈值，我们发现593种潜在的高介电材料。重要的是，我们分析了材料的化学性质，对称性，尺寸和几何形状，以找到有助于解释数据集中变化的特征。最后，我们针对最高红外频率和最大Born有效电荷开发了高精度回归模型，并针对最大压电和平均介电张量开发了分类模型，以加快发现速度。