Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials

TL;DR

This paper develops a data-driven framework using machine learning to discover new transparent conducting materials, addressing data scarcity and evaluating the models' ability to find novel candidates beyond known compositions.

Contribution

The study introduces a unique experimental database and a bespoke evaluation scheme to assess ML models' effectiveness in discovering new TCMs from composition data.

Findings

ML models tend to identify TCMs similar to training data

ML can highlight previously overlooked candidate materials

The framework offers a systematic approach for TCM discovery

Abstract

Machine Learning (ML) has offered innovative perspectives for accelerating the discovery of new functional materials, leveraging the increasing availability of material databases. Despite the promising advances, data-driven methods face constraints imposed by the quantity and quality of available data. Moreover, ML is often employed in tandem with simulated datasets originating from density functional theory (DFT), and assessed through in-sample evaluation schemes. This scenario raises questions about the practical utility of ML in uncovering new and significant material classes for industrial applications. Here, we propose a data-driven framework aimed at accelerating the discovery of new transparent conducting materials (TCMs), an important category of semiconductors with a wide range of applications. To mitigate the shortage of available data, we create and validate unique experimental databases, comprising several examples of existing TCMs. We assess state-of-the-art (SOTA) ML models for property prediction from the stoichiometry alone. We propose a bespoke evaluation scheme to provide empirical evidence on the ability of ML to uncover new, previously unseen materials of interest. We test our approach on a list of 55 compositions containing typical elements of known TCMs. Although our study indicates that ML tends to identify new TCMs compositionally similar to those in the training data, we empirically demonstrate that it can highlight material candidates that may have been previously overlooked, offering a systematic approach to identify materials that are likely to display TCMs characteristics.