Evaluating the transfer learning from metals to oxides with GAME-Net-Ox
GAME-Net-Ox, an extension of the GAME-Net graph neural network, enables fast prediction of adsorption energies for molecules with key organic functional groups on conductive and semiconductive rutile metal oxides and metal surfaces.
Authors
- Santiago Morandi (ORCID: https://orcid.org/0000-0003-0738-5612)
- Núria López (ORCID: https://orcid.org/0000-0001-9150-5941)
- Thomas van Hout
- Jordi Morales-Vidal
- Oliver Loveday
Institutions
- Institut Català d'Investigació Química (ES)
- Universitat Rovira i Virgili (ES)
- Eindhoven University of Technology (NL)
Publication Details
- Journal
- Digital Discovery
- Published
- 2025-12-03
- DOI
- https://doi.org/10.1039/d5dd00331h
- Citations
- 1
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 0.52
Funders
- Ministerio de Ciencia, Innovación y Universidades
- Generalitat de Catalunya
- Ministerio de Ciencia e Innovación
- Barcelona Supercomputing Center
- Agència de Gestió d'Ajuts Universitaris i de Recerca
- Agencia Estatal de Investigación