Automated Discovery of Wurtzite Solid Solutions with Enhanced Piezoelectric Response

TL;DR

This paper presents a systematic computational approach combining machine learning, chemical intuition, and thermodynamics to discover new doped wurtzite solid solutions with enhanced piezoelectric properties, exploring over 3000 candidates.

Contribution

It introduces a multi-level screening protocol that efficiently identifies promising dopant combinations in wurtzite materials for improved piezoelectric response.

Findings

Predicted 11 new materials with enhanced piezoelectric response.

Screened over 3000 potential ternary wurtzite solid solutions.

Validated improvements through thermodynamic and response calculations.

Abstract

While many piezoelectric materials are known, there is still great potential to improve on the figures of merit of existing materials through compositional doping, forming solid solutions. Specifically, it has been shown that doping and alloying wurtzite-structured materials can improve the piezoelectric response; however, a vast compositional space has remained unexplored. In this work, we apply a multi-level screening protocol combining machine learning, chemical intuition, and thermodynamics to systematically discover dopant combinations in wurtzite material space that improve the desired piezoelectric response. Through our protocol, we use computationally inexpensive screening calculations to consider more than 3000 possible ternary wurtzite solid solutions from 9 different wurtzite base systems: AlN, BeO, CdS, CdSe, GaN, ZnO, ZnS, ZnSe, and AgI. Finally, based on thermodynamic analysis and explicit piezoelectric response calculations, we predict 11 materials with improved piezoelectric response, due to the incorporation of electropositive dopants.