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Many technological applications depend on the response of materials toelectric fields, but available databases of such responses are limited. Here,we explore the infrared, piezoelectric and dielectric properties of inorganicmaterials by combining high-throughput density functional perturbation theoryand machine learning approaches. We compute {\Gamma}-point phonons, infraredintensities, Born-effective charges, piezoelectric, and dielectric tensors for5015 non-metallic materials in the JARVIS-DFT database. We find 3230 and 1943materials with at least one far and mid-infrared mode, respectively. Weidentify 577 high-piezoelectric materials, using a threshold of 0.5 C/m2. Usinga threshold of 20, we find 593 potential high-dielectric materials.Importantly, we analyze the chemistry, symmetry, dimensionality, and geometryof the materials to find features that help explain variations in our datasets.Finally, we develop high-accuracy regression models for the highest infraredfrequency and maximum Born-effective charges, and classification models formaximum piezoelectric and average dielectric tensors to accelerate discovery.

High-throughput Density Functional Perturbation Theory and Machine Learning Predictions of Infrared, Piezoelectric and Dielectric Responses