Active Learning Guided Design Space Refinement for Scalable Multi-Objective Bayesian Optimization in Materials Discovery

2026-08-05

AI Summary

This work addresses the inefficiency of conventional Bayesian optimization in large-scale discrete material design spaces, where evaluation budgets are often wasted. To overcome this limitation, the authors propose an active learning–guided adaptive design space refinement mechanism integrated with multi-objective Bayesian optimization. This approach dynamically prunes the candidate space during iterative optimization while preserving Pareto front fidelity with high accuracy. Empirical results demonstrate substantial gains in efficiency: in case studies on COF-based methane/nitrogen separation and pressure vessel design, the method reduces the candidate space by approximately 50% while retaining over 99% of the original hypervolume. Furthermore, it accelerates early convergence and facilitates the discovery of high-quality Pareto-optimal solutions.

Abstract

Advanced materials discovery increasingly relies on machine learning and Bayesian optimization to explore large discrete design spaces under limited evaluation budgets. However, conventional Bayesian optimization (BO) can become inefficient as candidate spaces grow, often evaluating low-value regions before reaching informative areas. We propose an active-learning (AL)-guided adaptive search-space refinement framework combined with multi-objective BO to accelerate materials optimization while preserving Pareto-relevant regions. We evaluate the approach on CH4/N2 separation in covalent-organic frameworks and pressure-vessel design with material-direction stress components and thickness objectives. Results show that the AL-guided refinement reduces the candidate space by approximately half while preserving more than 99 percent of the original hypervolume. The reduced-space strategy improves early convergence and cumulative Pareto-front discovery from the BO, demonstrating efficient large-scale materials optimization across constrained autonomous materials discovery settings.

Problem

• Bayesian optimization
• active learning
• materials discovery
• design space refinement
• multi-objective optimization

Innovation

• active learning
• design space refinement
• multi-objective Bayesian optimization
• Pareto front preservation
• materials discovery