Assessing data-driven predictions of band gap and electrical conductivity for transparent conducting materials
We curate experimental datasets and use them to train ML models for accelerating the identification of transparent conductors, demonstrating how ML can reveal candidates that may have been previously overlooked as viable transparent conductors.
Authors
- Federico Ottomano (ORCID: https://orcid.org/0009-0009-9005-5948)
- Michael W. Gaultois (ORCID: https://orcid.org/0000-0003-2172-2507)
- Luke M. Daniels (ORCID: https://orcid.org/0000-0002-7077-6125)
- Troy D. Manning (ORCID: https://orcid.org/0000-0002-7624-4306)
- D E Rimmer
- Emma Poole (ORCID: https://orcid.org/0000-0002-0563-1069)
- Matthew J. Rosseinsky (ORCID: https://orcid.org/0000-0002-7077-6125)
- Vladimir V. Gusev (ORCID: https://orcid.org/0000-0002-2815-607X)
- Teresa Partida Manzanera (ORCID: https://orcid.org/0000-0002-2834-6340)
- John Y. Goulermas (ORCID: https://orcid.org/0000-0003-0381-124X)
- Jonathan Alaria (ORCID: https://orcid.org/0000-0001-5868-0318)
- Rahul Savani (ORCID: https://orcid.org/0000-0003-1262-7831)
- Kevin D. Sanderson
- John B. Claridge (ORCID: https://orcid.org/0000-0003-4849-6714)
- Matthew S. Dyer (ORCID: https://orcid.org/0000-0002-4923-3003)
- Hai Lin (ORCID: https://orcid.org/0000-0002-6139-4563)
- Su Varma
Institutions
- Turing Institute (GB)
- University of Liverpool (GB)
- Pilkington (United Kingdom) (GB)
Publication Details
- Journal
- Digital Discovery
- Published
- 2025-01-01
- DOI
- https://doi.org/10.1039/d5dd00010f
- Citations
- 1
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.52
Funders
- Leverhulme Trust
- Leverhulme Research Centre for Functional Materials Design
- Engineering and Physical Sciences Research Council