Towards the object-oriented design of active hydrogen evolution catalysts on single-atom alloys

Area of Science:

• Materials Science
• Computational Chemistry
• Catalysis

Background:

• Identifying optimal materials with desired properties, such as for heterogeneous catalysis, is a significant challenge.
• Object-oriented design and screening offer a promising approach to accelerate materials discovery.
• The hydrogen evolution reaction (HER) is crucial for sustainable energy technologies like water electrolysis.

Purpose of the Study:

• To develop an inverse catalyst design workflow (CATIDPy) for accelerated identification of active single-atom alloy (SAA) catalysts.
• To utilize a genetic-algorithm-based global optimization method coupled with on-the-fly density functional theory (DFT) calculations.
• To demonstrate the workflow's capability in exploring vast chemical spaces for catalyst design.

Main Methods:

• Development of the CATIDPy Python workflow integrating genetic algorithms and DFT.
• Screening of 70 binary and 752 ternary single-atom alloy (SAA) candidates for HER activity.
• Inclusion of segregation stability and material cost criteria to refine candidate selection.
• Experimental synthesis and characterization of promising Ni-based bimetallic catalysts.

Main Results:

• Identification of 70 binary and 752 ternary SAA candidates for HER.
• Selection of 6 binary and 142 ternary SAA candidates based on stability and cost.
• Successful experimental synthesis of Ni-based bimetallic catalysts (NiMo, NiAl, Ni3Al, NiGa, NiIn).
• Synthesized catalysts exhibited superior HER performance compared to bare Ni foam.

Conclusions:

• The CATIDPy workflow significantly accelerates the discovery of active SAA catalysts for HER.
• The identified Ni-based catalysts show potential for practical applications in water electrolysis.
• This computational approach enables efficient exploration of unexplored chemical spaces for catalyst design, reducing reliance on expert knowledge.