Authors
Chaeyoung Park, Yeongje Lee, Sunho Jeong, Eungkyu Lee
Published in
Advanced science (Weinheim, Baden-Wurttemberg, Germany). Pages e76607. Jul 17, 2026. Epub Jul 17, 2026.
Abstract
Silicon suboxide (SiOx) 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 SiOx 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.
PMID:
42467971
Bibliographic data and abstract were imported from PubMed on 18 Jul 2026.
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