Atomic-level design principles for the hydrogen evolution reaction on high-entropy MXene catalysts

Area of Science:

• Materials Science
• Catalysis
• Computational Chemistry

Background:

• High-entropy materials (HEMs) offer tunable catalytic properties due to compositional diversity and synergistic metal interactions.
• The heterogeneity in HEMs creates varied local atomic environments and binding energies for catalytic intermediates.
• MXenes, particularly high-entropy MXenes (HE-MXenes), are promising for electrocatalysis, but their design principles require further investigation.

Purpose of the Study:

• To investigate the hydrogen evolution reaction (HER) on HE-MXenes.
• To uncover atomic-scale design principles governing HER catalytic activity in HE-MXenes.
• To establish a data-driven platform for discovering novel HE-MXene electrocatalysts.

Main Methods:

• High-throughput density functional theory (DFT) calculations were employed to study hydrogen adsorption on TiVNbMoC3O2 surfaces.
• A diverse set of local atomic environments were systematically probed.
• Machine learning models were utilized to identify key descriptors for catalytic activity.

Main Results:

• Catalytic behavior in HE-MXenes is primarily governed by local atomic coordination, not individual metal identity.
• The first-nearest neighbor shell to surface oxygen termination significantly influences HER activity, with activity trend V < Mo < Ti < Nb.
• Average covalent radius of neighboring metal atoms was identified as a key descriptor for adsorption behavior.

Conclusions:

• Local atomic environments, particularly the first-nearest neighbor shell, are crucial for HER activity in HE-MXenes.
• Machine learning enables efficient, descriptor-based screening of HE-MXene catalysts.
• This work accelerates the discovery of HE-MXenes for next-generation electrocatalysis.