Generative Artificial Intelligence for Designing Multi-Scale Hydrogen Fuel Cell Catalyst Layer Nanostructures.

Area of Science:

• Materials Science
• Chemical Engineering
• Artificial Intelligence

Background:

• Multiscale design of catalyst layers (CLs) is crucial for advancing hydrogen electrochemical conversion devices.
• Current limitations include complex component interactions, high synthesis costs, and vast design spaces, hindering rational design and optimization.
• Existing methods lack accurate nanostructure-performance relationship reflection and cost-effective design space exploration.

Purpose of the Study:

• To develop a deep generative artificial intelligence (AI) framework, GLIDER, for efficient multiscale CL nanostructure design and optimization.
• To integrate generative AI, data-driven surrogate techniques, and collective intelligence for performance-driven optimization.
• To address the need for rational design techniques that accurately link nanostructure to performance and efficiently search the design space.

Main Methods:

• Developed GLIDER, a deep generative AI framework utilizing a quantized vector-variational autoencoder for realistic multiscale CL digital generation.
• Leveraged dimensionality reduction for efficient nanostructure representation and generation.
• Integrated generative AI, data-driven surrogates, and collective intelligence for design space search.

Main Results:

• GLIDER enables realistic multiscale CL digital generation, capturing complex nanostructure-performance relationships.
• The framework efficiently searches optimal design parameters for Pt-carbon-ionomer nanostructures in CLs.
• Demonstrated transferability of GLIDER to other fuel cell electrode microstructures, including gas diffusion layers and solid oxide fuel cell anodes.

Conclusions:

• GLIDER provides a powerful AI-driven approach for the rational design and optimization of CLs.
• The framework significantly enhances the efficiency of searching optimal nanostructures based on electrochemical performance.
• GLIDER shows potential as a versatile digital tool for designing and optimizing a wide range of electrochemical energy devices.