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physics.chem-ph2026
A strategic roadmap for an atomistic machine-learning ecosystem
Jörg Behler, Michele Ceriotti, Cecilia Clementi +47
Data-driven machine learning (ML) techniques have become an essential tool in many domains of science. Their application to atomistic simulations of matter is particularly widespre…
physics.chem-ph2026
Multivariate conformal uncertainty propagation in multitask atomistic simulation: Successes and pitfalls
Katharine Fisher, Michael Herbst, James Kermode +1
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…