发表机构
National Centre for Scientific Research “Demokritos”（希腊国家科学研究中心“德谟克利特”）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出主动学习引导的自适应搜索空间优化框架，结合多目标贝叶斯优化，在两类材料优化任务中验证其可高效缩减候选空间并保留关键优化区域，提升大规模材料优化效率。
AI 中文摘要
先进材料发现日益依赖机器学习与贝叶斯优化，以在有限评估预算下探索大型离散设计空间。然而，随着候选空间扩大，传统贝叶斯优化（BO）效率会降低，常先评估低价值区域再到达信息丰富区域。我们提出主动学习（AL）引导的自适应搜索空间优化框架，结合多目标贝叶斯优化，在保留帕累托相关区域的同时加速材料优化。我们在共价有机框架的CH4/N2分离，以及含材料方向应力分量和厚度目标的压力容器设计上评估该方法。结果显示，AL引导的优化将候选空间缩小约一半，同时保留超过99%的原始超体积。该降空间策略改善了贝叶斯优化的早期收敛性与累积帕累托前沿发现，在受限自主材料发现场景中展现出高效的大规模材料优化能力。
英文摘要
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.
