Predicting the HER activity of SACs on MXenes with simple features and interpretable machine learning models
An interpretable machine learning model was developed using DFT data to predict hydrogen adsorption energies, enabling accurate and transparent screening of HER catalysts with performance comparable to the intrinsic accuracy of DFT methods.
Authors
- Chandra Bhusan Chowdhury
- Livia Giordano (ORCID: https://orcid.org/0000-0002-6879-9424)
- Francesc Viñes
- Gianfranco Pacchioni
- Giovanni Di Liberto
- Matteo Lovato
- Francesc Illas
Institutions
- University of Milano-Bicocca (IT)
- Universitat de Barcelona (ES)
Publication Details
- Journal
- Journal of Materials Chemistry A
- Published
- 2025-12-24
- DOI
- https://doi.org/10.1039/d5ta07143g
- Citations
- 3
- Primary Topic
- MXene and MAX Phase Materials
- Type
- article
- Field-Weighted Citation Impact
- 1.55
Funders
- European Commission
- Generalitat de Catalunya
- Institució Catalana de Recerca i Estudis Avançats
- Ministerio de Ciencia e Innovación
- Agencia Estatal de Investigación