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

TL;DR

This study combines high-throughput density functional perturbation theory and machine learning to predict infrared, piezoelectric, and dielectric properties of thousands of inorganic materials, aiding rapid materials discovery.

Contribution

It introduces a comprehensive computational and machine learning framework to evaluate and predict key electric response properties of inorganic materials at scale.

Findings

Identified 577 high-piezoelectric materials.

Found 593 potential high-dielectric materials.

Developed accurate models for infrared frequency and Born-effective charges.

Abstract

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.