Ottomano, Federico, Goulermas, John Y, Gusev, Vladimir 
ORCID: 0000-0002-2815-607X, Savani, Rahul ORCID: 0000-0003-1262-7831, Gaultois, Michael W ORCID: 0000-0003-2172-2507, Manning, Troy D ORCID: 0000-0002-7624-4306, Lin, Hai, Manzanera, Teresa Partida, Poole, Emmeline G, Dyer, Matthew S ORCID: 0000-0002-4923-3003 et al (show 7 more authors)
(2025)
Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials
DIGITAL DISCOVERY, 4 (7).
pp. 1794-1811.
ISSN 2635-098X, 2635-098X
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.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | 46 Information and Computing Sciences, 4607 Graphics, Augmented Reality and Games, Machine Learning and Artificial Intelligence, Networking and Information Technology R&D (NITRD) |
| Divisions: | Faculty of Science & Engineering Faculty of Science & Engineering > School of Electrical Engineering, Electronics and Computer Science Faculty of Science & Engineering > School of Physical Sciences |
| Depositing User: | Symplectic Admin |
| Date Deposited: | 17 Jun 2025 07:29 |
| Last Modified: | 23 May 2026 09:42 |
| DOI: | 10.1039/d5dd00010f |
| Open Access URL: | https://pubs.rsc.org/en/content/articlepdf/2025/dd... |
| Related Websites: | |
| URI: | https://livrepository.liverpool.ac.uk/id/eprint/3193269 |
| Disclaimer: | The University of Liverpool is not responsible for content contained on other websites from links within repository metadata. Please contact us if you notice anything that appears incorrect or inappropriate. |
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