Dresden 2026 – scientific programme

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MM: Fachverband Metall- und Materialphysik

MM 19: Poster Session

MM 19.1: Poster

Tuesday, March 10, 2026, 18:00–20:00, P5

RuNNer 2.0: A Fast Software Environment forHigh-Dimensional Neural Network Potentials — •Moritz R. Schäfer^{1, 2}, Alexander L. M. Knoll^{1, 2}, J. Richard Springborn^{1, 2}, Henry Wang^{1, 2}, K. Nikolas Lausch^{1, 2}, Moritz Gubler³, Jonas A. Finkler⁴, Gunnar Schmitz^{1, 2}, Alea Miako Tokita^{1, 2}, Emir Kocer^{1, 2}, and Jörg Behler^{1, 2} — ¹Theoretische Chemie II, Ruhr-Universität Bochum, Germany — ²Research Center Chemical Sciences and Sustainability, Research Alliance Ruhr, Germany — ³Paul Scherrer Institute PSI, Villigen, Switzerland — ⁴Department of Chemistry and Bioscience, Aalborg University, Denmark

Machine learning potentials (MLPs) have emerged as a widely used approach for large-scale atomistic simulations in chemistry and materials science. They offer computationally efficient access to highly accurate potential energy surfaces (PES) derived from ab initio reference data. As techniques in this area continue to grow in complexity and reach greater maturity, the need for robust, efficient, and user-friendly tools becomes increasingly significant. Here, we introduce the second major release of RuNNer, an open-source, stand-alone software package designed for constructing and evaluating second-, third-, and fourth-generation high-dimensional neural network potentials (HDNNPs). RuNNer 2.0 integrates the complete workflow into a fully MPI-parallelized program – from generating atomistic descriptors and training machine learning models to deploying them in molecular dynamics simulations.

Keywords: Software; Implementation; Machine Learning Potentials; Fortran; High-Dimensional Neural Network Potentials