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We benchmark the novel adaptive-MFML across diverse chemical properties including the computational chemistry gold standard coupl","title":"Improvise, Adapt, Overcome: An On-The-Fly Multifidelity Algorithm for Efficient Machine Learning","url":"https://arxiv.org/abs/2606.02662","vendor":"arxiv_cs_ai"},"summary":"arXiv:2606.02662v1 Announce Type: cross \nAbstract: Machine learning has accelerated quantum chemistry but is hindered by the prohibitive cost of generating high fidelity training data. Multifidelity machine learning (MFML) mitigates this overhead by systematically combining abundant low fidelity data with sparse high fidelity data. In spite of its success, standard MFML schemes rely on pre-defined scaling factors to determine sparse data ratio across fidelities, often generating redundant multifidelity data resulting in a loss of efficiency. 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