Human‐Guided Bayesian Optimization Enables High‐Throughput Laser Annealing of Mesoporous SiO _x Anodes for Lithium‐Ion Batteries

ABSTRACT Silicon suboxide (SiO _x ) has attracted significant attention as a promising anode material for next‐generation lithium‐ion batteries due to its high capacity and improved structural stability. However, precise control over stoichiometry and pore structure remains challenging under conventional thermal annealing. Herein, we introduce an empirical‐aided active learning (EAAL) approach to optimize high‐throughput laser‐induced photothermal annealing of high‐performance SiO _x anodes. By synergistically combining probabilistic machine learning with empirical domain knowledge, the EAAL framework efficiently explores complex processing parameter spaces using a limited number of experiments. Remarkably, the EAAL‐optimized anode achieves electrochemical performance comparable to that of an empirically optimized counterpart while significantly reducing the number of laser irradiation passes from five to two, thereby improving practical production throughput in cumulative laser‐processing. Furthermore, the optimal conditions are identified using only 26 experimental data points, significantly improving process efficiency. This study demonstrates a data‐efficient strategy for process optimization by synergistically integrating machine learning with empirical domain knowledge and provides a scalable pathway for the advanced manufacturing of high‐performance energy storage materials.