Atomic-level design principles for the hydrogen evolution reaction on high-entropy MXene catalysts
Abstract
exhibit particularly outstanding performance. To elucidate the underlying factors, we employ machine learning and identify the average covalent radius of neighboring metal atoms as a key descriptor governing adsorption behavior. This insight enables rapid, descriptor-based screening of high-performance HER catalysts without the need for exhaustive DFT calculations. Overall, our work highlights the pivotal role of local atomic environments in catalytic activity and establishes an efficient, data-driven platform to accelerate the discovery of HE-MXenes for next-generation electrocatalysis.
Fields of Study & Subjects
High Entropy Alloys Studies
MXene and MAX Phase Materials
Electrocatalysts for Energy Conversion
Publication details
- Journal / source
- Nanoscale
- Publisher
- Royal Society of Chemistry (RSC)
- Volume
- 18
- Issue
- 26
- Pages
- 13916–13924
- Publication date
- 1 January 2026
- Language
- EN