533 citations · 580 across the 12 of their papers we have counts for
13 papers
RuNNer 2.0: A Software Suite for High-Dimensional Neural Network Potentials
Alexander L. M. Knoll, Moritz R. Schäfer, K. Nikolas Lausch +11
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…
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…
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-…
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…
Noise Tolerant Force Calculations in Density Functional Theory: A Surface Integral Approach for Wavelet-Based Methods
Moritz Gubler, Jonas A. Finkler, Stig Rune Jensen +2
We introduce a method for computing quantum mechanical forces through surface integrals over the stress tensor within the framework of density functional theory. This approach avoi…
Accelerating fourth-generation machine learning potentials by quasi-linear scaling particle mesh charge equilibration
Moritz Gubler, Jonas A. Finkler, Moritz R. Schäfer +2
Machine learning potentials (MLP) have revolutionized the field of atomistic simulations by describing the atomic interactions with the accuracy of electronic structure methods at…
Ternary Phase Diagram of Nitrogen Doped Lutetium Hydrides
Moritz Gubler, Marco Krummenacher, Jonas A. Finkler +1
This paper presents the results of an extensive structural search of ternary solids containing lutetium, nitrogen and hydrogen. Based on thousands of thermodynamically stable struc…
Accurate Fourth-Generation Machine Learning Potentials by Electrostatic Embedding
Tsz Wai Ko, Jonas A. Finkler, Stefan Goedecker +1
In recent years, significant progress has been made in the development of machine learning potentials (MLPs) for atomistic simulations with applications in many fields from chemist…
A Fourth-Generation High-Dimensional Neural Network Potential with Accurate Electrostatics Including Non-local Charge Transfer
Tsz Wai Ko, Jonas A. Finkler, Stefan Goedecker +1
Machine learning potentials have become an important tool for atomistic simulations in many fields, from chemistry via molecular biology to materials science. Most of the establish…
Detecting non-local effects in the electronic structure of a simple covalent system with machine learning methods
Behnam Parsaeifard, Jonas A. Finkler, Stefan Goedecker
Using methods borrowed from machine learning we detect in a fully algorithmic way long range effects on local physical properties in a simple covalent system of carbon atoms. The f…
Funnel Hopping Monte Carlo: An efficient method to overcome broken ergodicity
Jonas A. Finkler, Stefan Goedecker
Monte Carlo simulations are a powerful tool to investigate the thermodynamic properties of atomic systems. In practice however, sampling of the complete configuration space is ofte…