  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 {\Gamma}-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.
