用主动学习精简材料设计空间，加速多目标优化
Active Learning Guided Design Space Refinement for Scalable Multi-Objective Bayesian Optimization in Materials Discovery
- 结合主动学习与多目标贝叶斯优化，动态缩小候选空间
- 空间缩减约一半，保留原超体积99%以上
- 适合资源受限的自动化材料发现场景
先进材料发现越来越多依赖机器学习与贝叶斯优化，在评估预算有限的情况下探索大规模离散设计空间。然而，传统贝叶斯优化（BO）在候选空间扩大时效率下降，常在信息量低的区域进行无效评估。本文提出一种主动学习（AL）引导的自适应搜索空间精简框架，结合多目标贝叶斯优化，加速材料优化并保留帕累托相关区域。在共价有机框架材料中甲烷/氮气分离及压力容器设计（含材料方向应力分量与厚度目标）任务上验证，该方法使候选空间减少约一半，同时保留超过99%的原始超体积。基于缩减空间的策略显著提升了早期收敛速度与累积帕累托前沿发现能力，展示了在约束性自主材料发现设置下的高效大规模优化性能。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。