99 citations · 333 across the 13 of their papers we have counts for
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How to Train a Shallow Ensemble
Moritz Schäfer, Matthias Kellner, Johannes Kästner +1
Shallow ensembles provide a convenient strategy for uncertainty quantification in machine learning interatomic potentials, that is computationally efficient because the different e…
Enhanced Representation-Based Sampling for the Efficient Generation of Datasets for Machine-Learned Interatomic Potentials
Moritz René Schäfer, Johannes Kästner
In this work, we present Enhanced Representation-Based Sampling (ERBS), a novel enhanced sampling method designed to generate structurally diverse training datasets for machine-lea…
Adaptive Transition State Refinement with Learned Equilibrium Flows
Samir Darouich, Vinh Tong, Tanja Bien +2
Identifying transition states (TSs), the high-energy configurations that molecules pass through during chemical reactions, is essential for understanding and designing chemical pro…
Apax: A Flexible and Performant Framework For The Development of Machine-Learned Interatomic Potentials
Moritz René Schäfer, Nico Segreto, Fabian Zills +2
We introduce Atomistic learned potentials in JAX (apax), a flexible and efficient open source software package for training and inference of machine-learned interatomic potentials.…
Instanton rate constant calculations using interpolated potential energy surfaces in non-redundant, rotationally and translationally invariant coordinates
Sean R. McConnell, Johannes Kästner
A trivial flaw in the utilization of artificial neural networks in interpolating chemical potential energy surfaces (PES) whose descriptors are Cartesian coordinates is their depen…