From Bottlenecks to Breakthroughs: How AI Is Reshaping Electrocatalysis (IMAGE)
Caption
This schematic illustrates the major bottlenecks that have long constrained electrocatalysis research—including the scale gap of atomistic simulations, limited inverse catalyst design, poor physical consistency of data-driven models, inefficient human experimentation, and scarce high-quality datasets—and how recent AI advances are helping to overcome them. Emerging approaches such as machine-learning interatomic potentials (MLIPs), diffusion-based generative models, physics-informed machine learning (PIML), autonomous robotic electrochemists, and FAIR-compliant data infrastructures are converging to transform electrocatalysis from a trial-and-error discipline into a predictive, data-centric discovery pipeline.
Credit
Richard G. Compton
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License
CC BY-NC-ND