arXiv subjects

K. Nikolas Lausch

Publications and source records attributed to K. Nikolas Lausch.

3 recordsLinked to original sources

arXiv2026

RuNNer 2.0: A Software Suite for High-Dimensional Neural Network Potentials

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 potentials (HDNNPs) of the second, third, and fourth generation. Long-range electrostatics and charge equilibration (QEq) for the description of non-local charge transfer in fourth-generation (4G) HDNNPs are accelerated by quasi-linear-scaling plane-wave methods, reducing QEq computational complexity from $\mathcal{O}(N^3)$ to $\mathcal{O}(N\log^2 N)$ such that linear or quasi-linear scaling is achieved across all HDNNP generations. An optimized memory management strategy eliminates the training overhead traditionally associated with long-range interactions, allowing 4G-HDNNPs to be trained with the same efficiency as their local counterparts. Developed in modern Fortran (2003/2008 standards), combined with a hybrid MPI/OpenMP parallelization scheme, RuNNer 2.0 has been designed to run efficiently in any CPU environment, from cost-effective local workstations to massive HPC clusters. Its modular library architecture facilitates straightforward binding to external simulation software; native interfaces to LAMMPS and the Atomic Simulation Environment (ASE) provide full access to all its features, including built-in committee-based uncertainty quantification. The high efficiency and scalability of the RuNNer 2.0 ecosystem are demonstrated through detailed benchmarks.

physics.chem-ph↗

arXiv2025

Impact of the damping function in dispersion-corrected density functional theory on the properties of liquid water

Accounting for dispersion interactions is essential in approximate density functional theory (DFT). Often, a correction potential based on the London formula is added, which is damped at short distances to avoid divergence and double counting of interactions treated locally by the exchange-correlation functional. Most commonly, two forms of damping, known as zero- and Becke-Johnson (BJ)-damping, are employed and it is generally assumed that the choice has only a minor impact on performance even though the resulting correction potentials differ quite dramatically. Recent studies have cast doubt on this assumption pointing to a significant effect of damping for liquid water, but the underlying reasons have not yet been investigated. Here, we analyze this effect in detail for the widely used Tkatchenko-Scheffler and DFT-D3 dispersion models. We demonstrate that, regardless of the dispersion model, both types of damping perform equally well for interaction energies of water clusters, but find that for the two investigated functionals zero-damping outperforms BJ-damping in dynamic simulations of liquid water. Compared to BJ-damping, zero-damping provides, e.g., an improved structural description, self-diffusion, and density of liquid water. This can be explained by the repulsive gradient at small distances resulting from damping to zero that artificially destabilizes water's tetrahedral hydrogen-bonding network. Therefore, zero-damping can compensate for deficiencies often observed for generalized gradient functionals, which is not possible for strictly attractive BJ-damping. Consequently, the improvement that can be achieved by applying a dispersion correction strongly depends on the employed damping function suggesting that the role of damping in dispersion-corrected DFT needs to be generally reevaluated.

physics.chem-ph↗

arXiv2024

Machine Learning Potentials for Heterogeneous Catalysis

The sustainable production of many bulk chemicals relies on heterogeneous catalysis. The rational design or improvement of the required catalysts critically depends on insights into the underlying mechanisms at the atomic scale. In recent years, substantial progress has been made in applying advanced experimental techniques to complex catalytic reactions in operando, but in order to achieve a comprehensive understanding, additional information from computer simulations is indispensable in many cases. In particular, ab initio molecular dynamics (AIMD) has become an important tool to explicitly address the atomistic level structure, dynamics, and reactivity of interfacial systems, but the high computational costs limit applications to systems consisting of at most a few hundred atoms for simulation times of up to tens of picoseconds. Rapid advances in the development of modern machine learning potentials (MLP) now offer a new approach to bridge this gap, enabling simulations of complex catalytic reactions with ab initio accuracy at a small fraction of the computational costs. In this perspective, we provide an overview of the current state of the art of applying MLPs to systems relevant for heterogeneous catalysis along with a discussion of the prospects for the use of MLPs in catalysis science in the years to come.

physics.chem-ph↗