arXiv subjects

Axel Groß

Publications and source records attributed to Axel Groß.

7 recordsLinked to original sources

arXiv2026

DISCOVER: A Physics-Informed, GPU-Accelerated Symbolic Regression Framework

Symbolic Regression (SR) enables the discovery of interpretable mathematical relationships from experimental and simulation data. These relationships are often coined descriptors which are defined as a fundamental materials property that is directly correlated to a desired or undesired functional property of the material. Although established approaches such as Sure Independence Screening and Sparsifying Operator (SISSO) have successfully identified low-dimensional descriptors within large feature spaces many existing SR tools integrate poorly with modern Python workflows, offer limited control over the symbolic search space, or struggle with the computational demands of large-scale studies. This paper introduces DISCOVER (Data-Informed Symbolic Combination of Operators for Variable Equation Regression), an open-source symbolic regression package developed to address these challenges through a modular, physics-motivated design. DISCOVER allows users to guide the symbolic search using domain knowledge, constrain the feature space explicitly, and take advantage of optional GPU acceleration to improve computational efficiency in data-intensive workflows, enabling reproducible and scalable SR workflows. The software is intended for applications in computational physics, computational chemistry, and materials science, where interpretability, physical consistency, and execution time are especially important, and it complements general-purpose SR frameworks by emphasizing the discovery of physically meaningful models.

physics.comp-ph↗

arXiv2023

The cycling mechanism of manganese-oxide cathodes in zinc batteries: A theory-based approach

Zinc-based batteries offer good volumetric energy densities and are compatible with environmentally friendly aqueous electrolytes. Zinc-ion batteries (ZIBs) rely on a lithium-ion-like Zn$^{2+}$-shuttle, which enables higher roundtrip efficiencies and better cycle life than zinc-air batteries. Manganese-oxide cathodes in near-neutral zinc sulfate electrolytes are the most prominent candidates for ZIBs. Zn$^{2+}$-insertion, H$^+$-insertion, and Mn$^{2+}$-dissolution are proposed to contribute to the charge-storage mechanism. During discharge and charge, two distinct phases are observed. Notably, the pH-driven precipitation of zinc-sulfate-hydroxide is detected during the second discharge phase. However, a complete and consistent understanding of the two-phase mechanism of these ZIBs is still missing. This paper presents a continuum full cell model supported by DFT calculations to investigate the implications of these observations. We integrate the complex-formation reactions of near-neutral aqueous electrolytes into the battery model and, in combination with the DFT calculations, draw a consistent picture of the cycling mechanism. We investigate the interplay between electrolyte pH and reaction mechanisms at the manganese-oxide cathodes and identify the dominant charge-storage mechanism. Our model is validated with electrochemical cycling data, cyclic voltammograms, and in-situ pH measurments. This allows us to analyse the influence of cell design and electrolyte composition on cycling and optimize the battery performance.

physics.chem-ph↗

arXiv2023

Oxide spinels with superior Mg conductivity

Mg batteries with oxide cathodes have the potential to significantly surpass existing Li-ion technologies in terms of sustainability, abundance, and energy density. However, Mg intercalation at the cathode is often severely hampered by the sluggish kinetics of Mg$^{2+}$ migration within oxides. Here we report a combined theoretical and experimental study addressing routes to identify cathode materials with an improved Mg-ion mobility. Using periodic density functional theory calculations, Mg$^{2+}$ migration in oxide spinels has been studied, revealing key features that influence the activation energy for Mg$^{2+}$ migration. Furthermore, the electronic and geometrical properties of the oxide spinels as well as their stability have been analyzed for a series of different transition metals in the spinels. We find that electronegative transition metals enable a high Mg-ion mobility in the oxide spinel frameworks and thus a favorable cathode functionality. Based on the theoretical findings, some promising candidates have been identified, prepared and structurally characterized. Our combined theoretical and experimental findings open up an avenue toward the utilization of functional cathode materials with improved Mg$^{2+}$ transport properties for Mg-metal batteries.

cond-mat.mtrl-sci↗

arXiv2022

FAIR data enabling new horizons for materials research

The prosperity and lifestyle of our society are very much governed by achievements in condensed matter physics, chemistry and materials science, because new products for sectors such as energy, the environment, health, mobility and information technology (IT) rely largely on improved or even new materials. Examples include solid-state lighting, touchscreens, batteries, implants, drug delivery and many more. The enormous amount of research data produced every day in these fields represents a gold mine of the twenty-first century. This gold mine is, however, of little value if these data are not comprehensively characterized and made available. How can we refine this feedstock; that is, turn data into knowledge and value? For this, a FAIR (findable, accessible, interoperable and reusable) data infrastructure is a must. Only then can data be readily shared and explored using data analytics and artificial intelligence (AI) methods. Making data 'findable and AI ready' (a forward-looking interpretation of the acronym) will change the way in which science is carried out today. In this Perspective, we discuss how we can prepare to make this happen for the field of materials science.

cond-mat.mtrl-sci↗

arXiv2021

Atomistic modeling of Li- and post-Li ion batteries

Alkali metal ion batteries, and in particular Li-ion batteries, have become a key technology for current and future energy storage, already nowadays powering many devices of our daily lives. Due to the inherent complexity of batteries and their components, the use of computational approaches on all length and time scales has been largely evolving within recent years. Gaining insight in complex processes or predicting new materials for specific applications are two of the main perspectives computational studies can offer, making them a indispensable tool of modern material science and hence battery research. After a short introduction to battery technology, this review will first focus on the theoretical concepts that underlie the functioning of Li- and post-Li-ion batteries. This will be followed by a discussion of the most prominent computational methods and their applications, currently available for the investigation of battery materials on an atomistic scale.

cond-mat.mtrl-sci↗

arXiv2018

Reaction energetics of Hydrogen on the Si(100) surface: A periodic many-electron theory study

We report on a many-electron wavefunction theory study for the reaction energetics of hydrogen dissociation on the Si(100) surface. We demonstrate that quantum chemical wavefunction based methods using periodic boundary conditions can predict chemically accurate results for the activation barrier and the chemisorption energy in agreement with experimental findings. These highly accurate results for the reaction energetics enable a deeper understanding of the underlying physical mechanism and make it possible to benchmark widely used density functional theory methods.

cond-mat.mtrl-sci↗

arXiv2010

Charged defects in graphene and the ionicity of hexagonal boron nitride in direct images

We report on the detection and charge distribution analysis for nitrogen substitutional dopants in single layer graphene membranes by aberration-corrected high-resolution transmission electron microscopy (HRTEM). Further, we show that the ionicity of single-layer hexagonal boron nitride can be confirmed from direct images. For the first time, we demonstrate by a combination of HRTEM experiments and first-principles electronic structure calculations that adjustments to the atomic potentials due to chemical bonding can be discerned in HRTEM images. Our experiments open a way to discern electronic configurations in point defects or other non-periodic arrangements or nanoscale objects that can not be analyzed in an electron or x-ray diffraction experiment.

cond-mat.mtrl-sci↗