Piezoelectric modulus prediction using machine learning and graph neural networks

TL;DR

This paper develops machine learning and graph neural network models to predict piezoelectric coefficients from material composition and structure, aiding the discovery of high-performance, environmentally friendly piezoelectric materials.

Contribution

It introduces a comprehensive approach combining feature engineering, traditional ML, and deep graph neural networks for accurate piezoelectric modulus prediction.

Findings

SVM with crystal structure features outperforms other models

Predicted top 20 high-performance piezoelectric materials from large database

Demonstrated effectiveness of graph neural networks in materials property prediction

Abstract

Piezoelectric materials are widely used in all kinds of industries such as electric cigarette lighters, diesel engines and x-ray shutters. However, discovering high-performance and environmentally friendly (e.g. lead-free) piezoelectric materials is a difficult problem due to the sophisticated relationships from materials' composition/structures to the piezoelectric effect. Compared to other material properties such as formation energy, band gap, and bulk modulus, it is much more challenging to predict piezoelectric coefficients. Here, we propose a comprehensive study on designing and evaluating advanced machine learning models for predicting the piezoelectric modulus from materials' composition and/or structures. We train the prediction models based on extensive feature engineering combined with machine learning models (Random Forest and Support Vector Machines) and automated feature learning based on deep graph neural networks. Our SVM model with crystal structure feature outperform other methods. We also use this model to predict the piezoelectric coefficients for 12,680 materials from the Materials Project database and report the top 20 potential high performance piezoelectric materials.