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20232026
most citedSingle-model uncertainty quantification in neural network potentials does not consistently outperform model ensembles

94 citations · 122 across the 11 of their papers we have counts for

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physics.comp-ph2026★ 1 cited

NATPS: Nonadiabatic Transition Path Sampling Using Time-Reversible MASH Dynamics

Xiran Yang, Madlen Maria Reiner, Brigitta Bachmair +3

Rare nonadiabatic events play a central role in photochemistry but remain difficult to simulate because excited-state dynamics is computationally demanding and often stochastic. He…

physics.comp-ph2025★ 3 cited

Incorporating Long-Range Interactions via the Multipole Expansion into Ground and Excited-State Molecular Simulations

Rhyan Barrett, Johannes C. B. Dietschreit, Julia Westermayr

Simulating long-range interactions remains a significant challenge for molecular machine learning potentials due to the need to accurately capture interactions over large spatial r…