Summary
The discovery and design of advanced functional materials often involve extremely large combinatorial design spaces, making exhaustive exploration through traditional computational or experimental approaches impractical. In recent years, machine learning and data-driven optimization techniques have emerged as promising tools for accelerating materials discovery by enabling efficient exploration of complex design spaces. Among these approaches, Bayesian Optimization (BO) has gained significant attention due to its ability to optimize expensive black-box functions using a limited number of evaluations. However, the performance of BO can deteriorate when applied to very large search spaces, as the algorithm must allocate evaluations across many potentially unpromising regions. To address this challenge, this thesis investigates the integration of Active Learning–based space reduction with Bayesian optimization for efficient materials design. Specifically, the proposed framework first applies Bayesian Optimization to explore the initial design space and gather informative samples. Subsequently, a classification-based Active Learning method (DAGS) is used to identify and filter low-performing regions of the design space, effectively reducing the search domain. Bayesian Optimization is then re-applied within the reduced design space in order to improve optimization efficiency. The proposed pipeline is evaluated in both single-objective and multi-objective optimization settings. Experimental results demonstrate that the proposed space-reduction strategy can significantly improve optimization efficiency by focusing the search on high-potential regions of the design space while maintaining competitive solution quality. The findings highlight the potential of combining Active Learning and Bayesian Optimization as a unified framework for data-efficient exploration of large materials design spaces, particularly in the context of nanoporous materials such as Covalent Organic Frameworks (COFs).
