  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.
