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New research explores multivariate conformal methods for atomistic simulation uncertainty

By PulseAugur Editorial · [1 sources] ·

A new research paper explores multivariate conformal methods for quantifying uncertainty in atomistic simulations, a crucial step for developing accurate interatomic potentials in machine learning. The study, authored by Katharine Fisher Schwab, introduces techniques like Bonferroni-corrected hyperrectangles and Mahalanobis distance-based sets to propagate uncertainty across multistage workflows. These methods aim to capture error cancellations in downstream quantities, improving predictions for chemical properties and atomistic configurations. AI

IMPACT Enhances the reliability of machine learning models in materials science by improving uncertainty quantification in atomistic simulations.

RANK_REASON Academic paper on a novel methodology for uncertainty quantification in ML for atomistic simulations. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv stat.ML →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New research explores multivariate conformal methods for atomistic simulation uncertainty

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Academic paper on a novel methodology for uncertainty quantification in ML for atomistic simulations. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv stat.ML TIER_1 English(EN) · Katharine Fisher, Michael Herbst, James Kermode, Youssef Marzouk ·

    Multivariate conformal uncertainty propagation in multitask atomistic simulation: Successes and pitfalls

    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…

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