arXiv subjects

Pjotrs Žguns

Publications and source records attributed to Pjotrs Žguns.

4 recordsLinked to original sources

arXiv2026

Local lattice dynamics of hcp zinc from EXAFS and machine-learning interatomic potentials

The lattice dynamics of hexagonal close-packed (hcp) zinc, a prototypical anisotropic metal, is studied using temperature-dependent Zn K-edge extended X-ray absorption fine structure (EXAFS) spectroscopy combined with atomistic simulations. The reverse Monte Carlo method enable the extraction of mean-square relative displacements (MSRDs) for eight coordination shells, providing a shell-resolved description of thermal motion. The MSRD temperature dependence, analysed using the correlated Einstein model, yields effective interatomic force constants and reveals pronounced anisotropy between in-plane and out-of-plane interactions. This anisotropy is further quantified by the ratio of MSRDs for the first and second coordination shells, which closely matches the anisotropic displacement parameters from diffraction experiments. Molecular dynamics simulations using the CHGNet universal machine-learning interatomic potential show that the original model overestimates thermal disorder, while a fine-tuned version substantially improves agreement with experimental EXAFS spectrum and radial distribution function. Overall, EXAFS-informed analysis is effective for validating and refining machine-learning interatomic potentials.

cond-mat.mtrl-sci↗

arXiv2026

Hydrogen in Brownmillerite Perovskites: First-Principles Insights into Energetics and Induced Electronic-Magnetic Changes

Hydrogen uptake in brownmillerite perovskites A2B2O5 offers an (electro)chemically accessible route to tune functional properties, but mechanistic understanding and design rules for hydrogen-responsive oxides remain limited. Here we employ density functional theory (DFT) to quantify how H absorption affects electronic structure, magnetic exchange, and anisotropy in representative Sr2Fe2O5 and Sr2Co2O5 oxides. We find that hydrogenation introduces a localized electron that stabilizes near the proton, with B-site-dependent preference. The resulting lattice distortions and redistribution of charge density modify exchange coupling and cant the Neel vector, giving rise to weak ferromagnetism. We also show that absorption energies are highly sensitive to proton-electron arrangements and magnetic order, varying by up to 1 eV across different settings. This sensitivity demands consistent treatment of charge localization and spin states, together with careful choice of computational parameters. Extending to a variety of experimentally reported A2B2O5 compositions, we identify candidates with favorable H uptake and uncover a trend linking more favorable absorption to a higher B-site d-electron count. We also demonstrate that the preferred proton absorption site in these materials is governed by local O-O separations and lattice flexibility, which describe the ability of the framework to accommodate proton-induced distortions. Finally, benchmarks of universal machine-learning interatomic potentials reveal uncertainties of about 1 eV for site-resolved absorption energies, motivating descriptor-based surrogate models and targeted DFT validation. Together, these results establish practical design rules for hydrogen-responsive oxides relevant to iono-electronic devices, sensors, and electrically tunable spin functionality.

cond-mat.mtrl-sci↗

arXiv2025

Polaron and Strain Effects on Ion Migration in WO$_3$

Ion migration in WO$_3$ is a critical process for various technological applications, such as in batteries, electrochromic devices and energy-efficient brain-inspired computing devices. In this study, we investigate the migration mechanisms of H$^+$, Li$^+$, and Mg$^{2+}$ ions in monoclinic WO$_3$, and how energy barriers are affected by the presence of electron polarons and by lattice strain. Our approach in calculating the migration paths and barriers is based on density functional theory methods. The results show that the presence of polarons leads to association effects and lattice deformations that increase ion migration barriers. Therefore, the consideration of polarons is critical to accurately predict activation energies of ion migration. We further show that lattice strain modulates ion migration barriers, however, the impact of strain depends on the migrating ion. For protons that are embedded in the oxygen ion electronic shells and hop from donor to acceptor oxygens, compressive lattice strain accelerates migration by reducing the donor-acceptor distance. In contrast, the migration barriers of larger ions decrease with tensile lattice strain that increases the free space for the ion in the transition state. These insights into the effects of polarons and lattice strain are important for understanding and tuning properties of WO$_3$ when aiming for optimized device characteristics.

cond-mat.mtrl-sci↗

arXiv2025

Benchmarking CHGNet Universal Machine Learning Interatomic Potential Against DFT and EXAFS: Case of Layered WS2 and MoS2

Universal machine learning interatomic potentials (uMLIPs) deliver near ab initio accuracy in energy and force calculations at low computational cost, making them invaluable for materials modeling. Although uMLIPs are pre-trained on vast ab initio datasets, rigorous validation remains essential for their ongoing adoption. In this study, we use the CHGNet uMLIP to model thermal disorder in isostructural layered 2Hc-WS2 and 2Hc-MoS2, benchmarking it against ab initio data and extended X-ray absorption fine structure (EXAFS) spectra, which capture thermal variations in bond lengths and angles. Fine-tuning CHGNet with compound-specific ab initio (DFT) data mitigates the systematic softening (i.e., force underestimation) typical of uMLIPs and simultaneously improves alignment between molecular dynamics-derived and experimental EXAFS spectra. While fine-tuning with a single DFT structure is viable, using ~100 structures is recommended to accurately reproduce EXAFS spectra and achieve DFT-level accuracy. Benchmarking the CHGNet uMLIP against both DFT and experimental EXAFS data reinforces confidence in its performance and provides guidance for determining optimal fine-tuning dataset sizes.

cond-mat.mtrl-sci↗