TY - JOUR
T1 - Active Learning-Driven Bayesian Optimization of Catalytic Nanoparticles for CO₂ Reduction
AU - Hiroshi Tanaka
AU - Yuki Sato
AU - Kenji Mori
AU - Rina Okabe
JF - Journal of Artificial Intelligence for Materials Science
JO - J. Artif. Intell. Mater. Sci.
SN - 3149-8957
Y1 - 2023
VL - 2
IS - 2
SP - 34
N2 - The escalating global challenge of carbon dioxide (CO₂) emissions necessitates innovative approaches to mitigate climate change through efficient catalytic conversion. This conceptual manuscript proposes a novel theoretical framework that integrates active learning with Bayesian optimization to enhance the design of catalytic nanoparticles for CO₂ reduction. Drawing on principles from machine learning and materials science, the framework addresses the complexities of high-dimensional parameter spaces in nanoparticle synthesis, such as size, shape, composition, and surface facets, which influence catalytic performance. By leveraging active learning to intelligently select informative data points and Bayesian optimization to refine surrogate models iteratively, the approach theoretically accelerates the identification of optimal nanoparticle configurations without empirical validation. The framework emphasizes uncertainty quantification and adaptive sampling to efficiently navigate the vast design space. This synthesis of concepts from recent literature highlights gaps in traditional optimization methods and posits that the proposed integration could conceptually reduce exploration costs while enhancing selectivity and activity in CO₂ reduction processes. The manuscript outlines theoretical underpinnings, a proposed framework, and implications for applied artificial intelligence in materials science, fostering future conceptual advancements in sustainable catalysis.
UR - https://iamrp.net/p457561086
ER -