Toward accelerated electrocatalyst design: synergistic integration of DFT, machine learning, and microkinetic...

Area of Science:

• Materials Science
• Computational Chemistry
• Electrochemistry

Background:

• Electrocatalyst design is crucial for sustainable energy but faces challenges due to complex mechanisms and vast material possibilities.
• Rational design requires advanced computational tools to navigate the complex chemical space and kinetics.

Purpose of the Study:

• To highlight the synergistic integration of DFT, ML, and MKM as a unified framework for accelerating electrocatalyst discovery.
• To review the roles of DFT, ML, and MKM in catalyst design and explore their combined applications.

Main Methods:

• Density Functional Theory (DFT) for elucidating electronic structures and reaction mechanisms.
• Machine Learning (ML) for high-throughput screening, property prediction, and autonomous discovery.
• Microkinetic Modeling (MKM) for bridging atomistic energetics with experimentally relevant quantities.

Main Results:

• The integrated DFT-ML-MKM framework enables rational design of single-atom, dual-atom, and multifunctional electrocatalysts.
• Applications demonstrated for key reactions including HER, OER, ORR, CO2RR, and NRR.
• Identified challenges and emerging opportunities in computational electrocatalyst discovery.

Conclusions:

• The synergistic integration of DFT, ML, and MKM offers a powerful approach to accelerate the discovery of next-generation electrocatalysts.
• Addressing challenges in data quality, model transferability, and multiscale integration is key for future advancements.
• Emerging AI techniques promise further breakthroughs in designing electrocatalysts with enhanced performance and stability.