新研究探索用于原子模拟不确定性的多变量共形方法

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-28 04:00

一篇新研究论文探讨了用于量化原子模拟中不确定性的多变量共形方法，这是开发机器学习中准确的原子间势的关键步骤。该研究由Katharine Fisher Schwab撰写，引入了诸如Bonferroni校正超矩形和基于Mahalanobis距离的集合等技术，以在多阶段工作流程中传播不确定性。这些方法旨在捕捉下游数量的误差抵消，从而改进化学性质和原子构型的预测。 AI

影响通过改进原子模拟中不确定性的量化，增强了材料科学中机器学习模型的可靠性。

排序理由关于用于原子模拟中机器学习的不确定性量化的新颖方法学的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv stat.ML 阅读 →

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报道来源 [1]

arXiv stat.ML TIER_1 English(EN) · Katharine Fisher, Michael Herbst, James Kermode, Youssef Marzouk · 2026-09-28 04:00

多变量一致性不确定性传播在多任务原子模拟中的应用：成功与陷阱

arXiv:2609.31384v1 Announce Type: cross Abstract: Machine learning has become the standard tool for the design of interatomic potentials which balance efficiency and accuracy, but uncertainty quantification remains an open problem. Multiscale simulations introduce an additional c…

本文如何被排名

报道来源 [1]

多变量一致性不确定性传播在多任务原子模拟中的应用：成功与陷阱

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