Adaptive MFML algorithm saturates accuracy at low fidelities before escalating, cutting data costs up to 30x vs single-fidelity and 5x vs standard MFML on coupled cluster and excitation energies.
Kristof T Schütt, Huziel E Sauceda, Pieter-Jan Kindermans, Alexandre Tkatchenko, and Klaus- Robert Müller
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Bipartite Cholesky Graph Networks from density-fitted ERI decomposition achieve 0.0296 Ha in-distribution MAE on six diatomic molecules under FCI reference, outperforming compressed-integral baselines, with generalization tied to orbital environment similarity.
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Improvise, Adapt, Overcome: An On-The-Fly Multifidelity Algorithm for Efficient Machine Learning
Adaptive MFML algorithm saturates accuracy at low fidelities before escalating, cutting data costs up to 30x vs single-fidelity and 5x vs standard MFML on coupled cluster and excitation energies.