35 citations · 56 across the 4 of their papers we have counts for
4 papers
Neural Integral Operators for Inverse Problems: An Operator-Learning Framework for Small-Sample Spectroscopic Classification
Emanuele Zappala, Alice Giola, Andreas Kramer +2
Learning maps between function spaces with a strong inductive bias is a central challenge in soft computing, especially when training data are scarce and standard deep architecture…
OpenMM 8: Molecular Dynamics Simulation with Machine Learning Potentials
Peter Eastman, Raimondas Galvelis, Raúl P. Peláez +22
Machine learning plays an important and growing role in molecular simulation. The newest version of the OpenMM molecular dynamics toolkit introduces new features to support the use…
Skipping the Replica Exchange Ladder with Normalizing Flows
Michele Invernizzi, Andreas Krämer, Cecilia Clementi +1
We combine replica exchange (parallel tempering) with normalizing flows, a class of deep generative models. These two sampling strategies complement each other, resulting in an eff…
Stochastic approximation to MBAR and TRAM: batch-wise free energy estimation
Maaike M. Galama, Hao Wu, Andreas Krämer +2
The dynamics of molecules are governed by rare event transitions between long-lived (metastable) states. To explore these transitions efficiently, many enhanced sampling protocols…