Shalini Tomar

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

Emerging opportunities in physics-informed machine learning, graph neural networks, generative artificial intelligence, active learning, and autonomous closed-loop DFT-ML-MKM workflows are discussed as promising directions for accelerating the discovery of next-generation electrocatalysts with enhanced activity, selectivity, and long-term stability.