Improvise, Adapt, Overcome: An On-The-Fly Multifidelity Algorithm for Efficient Machine Learning

In three linesAdaptive multifidelity algorithm for machine learning in quantum chemistry. Dynamically determines dataset composition by querying samples at each fidelity level. Reduces data generation costs by up to 30× versus single-fidelity methods and 5× versus standard MFML on coupled cluster energies and excitation energies.

Summary generated by Claude — human-verified