arXiv subjects

Max Großmann

Publications and source records attributed to Max Großmann.

8 recordsLinked to original sources

arXiv2026

Quantum nuclear and band-dispersion effects recover near-UV absorption in short-hydrogen-bonded organic crystals

Near-UV optical absorption is increasingly reported in hydrogen-bonded organic and biomolecular materials lacking aromatic or extended pi-conjugated chromophores, yet its microscopic origin remains unresolved and electronic-structure calculations often overestimate experimental absorption onsets. Here, we combine machine-learned interatomic potentials for large-scale classical and quantum nuclear sampling with periodic excited-state calculations to address this discrepancy in L-pyroglutamine ammonium, an experimentally established glutamine-derived crystal containing a well-resolved short hydrogen bond and exhibiting non-aromatic near-UV optical response. Using controlled in silico ion substitutions that vary the surrounding hydrogen-bond environment while preserving this scaffold, we compute optical spectra from configurations sampled along classical and quantum nuclear trajectories using hybrid-functional time-dependent density functional theory. We show that nuclear quantum effects stabilise proton-sharing configurations that are strongly suppressed classically, redshifting the lowest bright excitations by 0.5-0.8 eV and raising the fraction of configurations with bright excitations below 6 eV from approximately 3% to approximately 30%. Explicit Brillouin-zone sampling provides a further, mechanistically distinct redshift of 0.5-1.1 eV, reflecting modest but significant indirect electronic character. Only when both effects are incorporated does the calculated onset recover the experimental 3.8-4.5 eV range. These results establish quantum proton fluctuations and reciprocal-space convergence as cooperative but physically distinct ingredients required for predictive optical spectroscopy of strongly hydrogen-bonded molecular materials.

physics.chem-ph↗

arXiv2026

High-throughput screening and mechanistic insights into solid acid proton conductors

Proton-conducting solid acids could enable water-free operation of high-temperature fuel cells. However, systematic materials screening has, hitherto, been computationally prohibitive. Here, we introduce a two-stage high-throughput screening strategy that directly computes proton diffusion coefficients, enabled by machine-learned interatomic potentials fine-tuned to ab initio data. Starting from more than six million materials, our screening -- based on structural motifs rather than empirical descriptors -- identifies $27$ high-performing proton conductors, including over ten previously unexplored compounds. These include sustainable and commercially available materials, candidates that have not yet been synthesized, organic systems that fall outside conventional design rules, and known proton conductors that validate our approach. Importantly, our findings reveal a universal oxygen--oxygen distance of approximately $2.5$~{\AA} at the moment of proton transfer across diverse chemistries, providing mechanistic insight and showing that macroscopic proton conductivity emerges from the interplay between anion rotational dynamics, hydrogen-bond network connectivity, and proton-transfer probability.

physics.chem-ph↗

arXiv2026

Broken neural scaling laws in materials science

In materials science, data are scarce and expensive to generate, whether computationally or experimentally. Therefore, it is crucial to identify how model performance scales with dataset size and model capacity to distinguish between data- and model-limited regimes. Neural scaling laws provide a framework for quantifying this behavior and guide the design of materials datasets and machine learning architectures. Here, we investigate neural scaling laws for a paradigmatic materials science task: predicting the dielectric function of metals, a high-dimensional response that governs how solids interact with light. Using over 200,000 dielectric functions from high-throughput ab initio calculations, we study two multi-objective graph neural networks trained to predict the frequency-dependent complex interband dielectric function and the Drude frequency. We observe broken neural scaling laws with respect to dataset size, whereas scaling with the number of model parameters follows a simple power law that rapidly saturates.

cond-mat.mtrl-sci↗

arXiv2025

Many-body perturbation theory vs. density functional theory: A systematic benchmark for band gaps of solids

We benchmark many-body perturbation theory against density functional theory (DFT) for the band gaps of solids. We systematically compare four $GW$ variants $-$ $G_{0}W_{0}$ using the Godby-Needs plasmon-pole approximation ($G_{0}W_{0}$-PPA), full-frequency quasiparticle $G_{0}W_{0}$ (QP$G_{0}W_{0}$), full-frequency quasiparticle self-consistent $GW$ (QS$GW$), and QS$GW$ augmented with vertex corrections in $W$ (QS$G\hat{W}$) $-$ against the currently best performing and popular density functionals mBJ and HSE06. Our results show that $G_{0}W_{0}$-PPA calculations offer only a marginal accuracy gain over the best DFT methods, however at a higher cost. Replacing the PPA with a full-frequency integration of the dielectric screening improves the predictions dramatically, almost matching the accuracy of the QS$G\hat{W}$. The QS$GW$ removes starting-point bias, but systematically overestimates experimental gaps by about $15\%$. Adding vertex corrections to the screened Coulomb interaction, i.e., performing a QS$G\hat{W}$ calculation, eliminates the overestimation, producing band gaps that are so accurate that they even reliably flag questionable experimental measurements.

cond-mat.mtrl-sci↗

arXiv2025

On the origin of bulk-related anisotropies in surface optical spectra

Reflection anisotropy spectroscopy (RAS) is a powerful method for probing the optical properties of surfaces, used routinely in research and industrial applications, yet the origin of 'bulk-related' features that appear in the spectra of various surfaces has been debated for nearly 40 years. It is often argued that these features are related to surface-induced bulk anisotropy (SIBA) because they coincide with critical energies of the bulk dielectric function. In general, any quantitative RAS theory must include excitonic effects as they significantly influence the spectra and are believed to be the key to determining the origin of SIBA features. Here, we introduce a layer-resolved exciton localization (LREL) measure within the framework of many-body perturbation theory, which enables a quantitative analysis of the origins of 'bulk-related' RAS features. Applying LREL to arsenic-modified silicon reconstructions reveals that, depending on the surface reconstruction, the 'apparent' SIBA features arise primarily from states localized at the surface, with only a small contribution from the underlying layers. Our findings, further supported by the fact that the calculated spectra agree well with low-temperature RAS measurements, challenge the conventional explanation of 'bulk-related' RAS features. They indicate that in many instances bulk-enhanced surface anisotropies (BESA)-the opposite of SIBA-contribute to, or are even responsible for, 'bulk-related' RAS features. Therefore, we suggest that previously studied semiconductor surfaces, which exhibit 'bulk-related' features in their spectra, should be reanalyzed using the presented method.

cond-mat.mtrl-sci↗

arXiv2025

Discovery of sustainable energy materials via the machine-learned material space

Does a machine learning model actually gain an understanding of the material space? We answer this question in the affirmative on the example of the OptiMate model, a graph attention network trained to predict the optical properties of semiconductors and insulators. By applying the UMAP dimensionality reduction technique to its latent embeddings, we demonstrate that the model captures a nuanced and interpretable representation of the materials space, reflecting chemical and physical principles, without any user-induced bias. This enables clustering of almost 10,000 materials based on optical properties and chemical similarities. Beyond this understanding, we demonstrate how the learned material space can be used to identify more sustainable alternatives to critical materials in energy-related technologies, such as photovoltaics. These findings demonstrate the dual utility of machine learning models in materials science: Accurately predicting material properties while providing insights into the underlying materials space. The approach demonstrates the broader potential of leveraging learned materials spaces for the discovery and design of materials for diverse applications, and is easily applicable to any state-of-the-art machine learning model.

cond-mat.mtrl-sci↗

arXiv2025

Modelling complex proton transport phenomena -- Exploring the limits of fine-tuning and transferability of foundational machine-learned force fields

The solid acids CsH$_2$PO$_4$ and Cs$_7$(H$_4$PO$_4$)(H$_2$PO$_4$)$_8$ pose significant challenges for the simulation of proton transport phenomena. In this work, we use the recently developed machine-learned force field (MLFF) MACE to model the proton dynamics on nanosecond time scales for these systems and compare its performance with long-term ab initio molecular dynamics (AIMD) simulations. The MACE-MP-0 foundation model shows remarkable performance for all observables derived from molecular dynamics (MD) simulations, but minor quantitative discrepancies remain compared to the AIMD reference data. However, we show that minimal fine-tuning -- fitting to as little as 1 ps of AIMD data -- leads to full quantitative agreement between the radial distribution functions of MACE force field and AIMD simulations. In addition, we show that traditional long-term AIMD simulations fail to capture the correct qualitative trends in diffusion coefficients and activation energies for these solid acids due to the limited accessible time scale. In contrast, accurate and convergent diffusion coefficients can be reliably obtained through multi-nanosecond long MD simulations using machine-learned force fields. The obtained qualitative and quantitative behavior of the converged diffusion coefficients and activation energies now matches the experimental trends for both solid acids, in contrast to previous AIMD simulations that yielded a qualitatively wrong picture.

physics.comp-ph↗

arXiv2024

Deep learning of spectra: Predicting the dielectric function of semiconductors

Predicting spectra and related properties such as the dielectric function of crystalline materials based on machine learning has a huge, hitherto unexplored, technological potential. For this reason, we create an ab initio database of 9915 dielectric tensors of semiconductors and insulators calculated in the independent-particle approximation (IPA). In addition, we present the OptiMate family of machine learning models, a series of graph attention neural networks (GAT) trained to predict the dielectric function and refractive index. OptiMate yields accurate prediction of spectra of semiconductors using only their crystal structure. Smooth, artifact-free curves are obtained without these properties being enforced by penalties.

cond-mat.mtrl-sci↗