English(EN) Portfolio-Based Constrained Multi-Objective Bayesian Optimization for Materials Design

LLM驱动的自适应策略增强材料设计优化

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-18 04:00

研究人员开发了一种新颖的材料设计方法，将其构建为约束多目标贝叶斯优化问题。该方法利用自适应策略，特别是修改后的UCB多臂老虎机和由大型语言模型（LLM）驱动的多智能体决策系统，来动态选择采集函数。在合成基准和材料设计案例研究上的评估表明，这些自适应策略在发现可行候选物和改进帕累托前沿方面取得了有竞争力的结果，优于固定策略基线。 AI

影响这项研究展示了LLM如何集成到优化框架中，有可能加速材料科学和其他领域的科学发现。

排序理由详细介绍材料设计新优化方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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本文如何被排名

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报道来源 [1]

arXiv stat.ML TIER_1 English(EN) · Sushant Sinha, Christofer Hardcastle, Robert Robinson, Shakti Prasad Padhy, Brent Vela, Douglas Allaire, Raymundo Arroyave · 2026-09-18 04:00

面向材料设计的基于组合约束的多目标贝叶斯优化

arXiv:2609.19550v1 Announce Type: cross Abstract: Materials discovery and design campaigns can be formulated as constrained multi-objective Bayesian optimization (CMOBO) problems, within which each experimental decision negotiates between two coupled but competing goals: discover…

本文如何被排名

报道来源 [1]

面向材料设计的基于组合约束的多目标贝叶斯优化

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