English(EN) KolTa: A Physics-Informed Active Learning Framework for Formulation Discovery with Limited Data

KolTa框架通过物理信息主动学习加速配方发现

作者 PulseAugur 编辑部 · [1 个来源] · 2026-11-05 05:00

研究人员开发了KolTa，一个新颖的主动学习框架，旨在优化高性能配方的发现，尤其是在实验数据稀缺的情况下。该框架集成了多阶段预测器和特定任务的物理约束，并采用了一种称为物理残差校准上限置信度（PRC-UCB）的独特采集规则。在包括二元混合物粘度和药物溶解度在内的各种任务上的评估表明，KolTa的性能优越，优于八个比较策略中的七个，并显著减少了所需的配方查询数量。 AI

影响该框架可以显著降低发现新材料和化学配方所需的成本和时间。

排序理由该集群包含一篇详细介绍新框架和方法的论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Boyong Tang, Tao Wang, Lei Yang · 2026-11-05 05:00

KolTa：一种用于有限数据配方发现的物理信息主动学习框架

arXiv:2611.06394v1 Announce Type: new Abstract: Finding high-performing formulations is difficult when few have been measured and experimental budgets are limited. We propose KolTa, an active learning framework that selects batches to discover the top 5% of formulations at a targ…

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KolTa：一种用于有限数据配方发现的物理信息主动学习框架

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