7 papers
RuNNer 2.0: A Software Suite for High-Dimensional Neural Network Potentials
Alexander L. M. Knoll, Moritz R. Schäfer, K. Nikolas Lausch +10
We present RuNNer 2.0, the "Ruhr University Neural Network energy representation", a highly optimized software suite for training and evaluating high-dimensional neural network pot…
Inverse Design of Amorphous Materials with Targeted Properties
Jonas A. Finkler, Yan Lin, Tao Du +2
Disordered (amorphous) materials, such as glasses, are emerging as promising candidates for applications within energy storage, nonlinear optics, and catalysis. Their lack of long-…
AMShortcut: An Inference- and Training-Efficient Inverse Design Model for Amorphous Materials
Yan Lin, Jonas A. Finkler, Tao Du +2
Amorphous materials are solids that lack long-range atomic order but possess complex short- and medium-range order. Unlike crystalline materials that can be described by unit cells…
Planar Structures of Medium-Sized Gold Clusters Become Ground States upon Ionization
Mohammad Ismaeil Safa, Ehsan Rahmatizad Khajehpasha, Stefan Goedecker
This study investigates the structural stability of ionized gold clusters of sizes ranging from 22 to 100 atoms, contrasting compact, cage and planar structures. While it is well k…
Iterative charge equilibration for fourth-generation high-dimensional neural network potentials
Emir Kocer, Andreas Singraber, Jonas A. Finkler +4
Machine learning potentials (MLP) allow to perform large-scale molecular dynamics simulations with about the same accuracy as electronic structure calculations provided that the se…
Implications of the multi-minima character of molecular crystal phases onto the free energy
Marco Krummenacher, Martin Sommer-Jörgensen, Moritz Gubler +4
In recent years, significant advancements in computational methods have dramatically enhanced the precision in determining the energetic ranking of different phases of molecular cr…