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Daniel Pajerowski

Daniel Pajerowski contributes to research discovery and scholarly infrastructure.

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cond-mat.mtrl-sci Machine Learning cond-mat.str-el

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preprint2026arXiv

Coupled sawtooth chain exchange network in olivine Mn$_2$GeO$_4$

Sawtooth chain magnets have been a subject of historical interest in the field of frustrated magnetism, with classical olivine family $M_2TX_4$, ($M$ - 3d, $T$ - 4p, $X$ - chalcogen elements) typically realizing simple $\mathbf{k} = (000)$ states. The magnetism of the Mn$_2$GeO$_4$ olivine is surprisingly complex, proceeding from commensurate states to a multiferroic commensurate + incommensurate phase. Here we report inelastic neutron scattering results from a Mn$_2$GeO$_4$ single crystal and develop an effective Hamiltonian including long-distance bilinear and dipolar interactions. The magnetic interactions are predominantly antiferromagnetic and span a three-dimensional exchange network consisting of coupled sawtooth chains. Based on the determined strength of the couplings, the dominant sawtooth chains appear at third- and fourth- rather than next-nearest-neighbor. However the next-nearest-neighbor interaction is, along with a modest Dzyaloshinskii-Moriya interaction, important for modeling the observed incommensurability. We use the best-fit Hamiltonian as the basis for Langevin dynamics simulations and Luttinger-Tisza calculations of the high-temperature commensurate transition.

preprint2026arXiv

Probing Non-Equilibrium Grain Boundary Dynamics with XPCS and Domain-Adaptive Machine Learning

Grain-boundary (GB) dynamics control the stability, mechanical, and functional response of nanocrystalline materials, but direct experimental access to their slow non-equilibrium motion has been limited. Here we establish X-ray photon correlation spectroscopy (XPCS), combined with domain-adaptive machine learning, as a quantitative probe of GB dynamics. Temperature- and grain-size-dependent two-time XPCS measurements in nanocrystalline silicon reveal pronounced departures from time-translation invariance, showing that GB relaxation can remain far from equilibrium over experimental timescales. However, direct extraction of quantitative physical information from these high-dimensional, noisy fluctuation maps faces a significant challenge. To overcome this barrier, we develop a semi-supervised learning framework that transfers physical parameter labels from continuum simulations to unlabeled experimental XPCS maps through domain-adaptive representation alignment. This AI-augmented approach enables the extraction of key kinetic parameters, including bulk diffusivity, GB stiffness, and effective GB concentration, directly from experimental XPCS measurements. Our results show how machine learning can transform indirect fluctuation signals into quantitative materials dynamics, providing a general route to study non-equilibrium defect motion in solids.

preprint2026arXiv

Universal Magnetic Structure Prediction from Atomic Coordinates with Near-Experimental Accuracy

Magnetic order is a fundamental property of materials, governing collective behavior and enabling a broad range of functionalities. Yet magnetic structure remains difficult to determine: experiments are costly and specialized, while first-principles methods often struggle with the noncollinear and incommensurate orders found in real materials. Here we introduce magnetic structure network (MSN), an E(3) equivariant graph neural network that predicts both collinear and non-collinear magnetic structures directly from atomic crystal structures, trained directly on experimentally determined structures from MAGNDATA. By proposing the primitive modulated structure representation (PMSR), we are able to encode commensurate and incommensurate structures in a unified way without symmetry assumptions. The model achieves strong performance across all modulation components and reconstructs experimental magnetic structures with high fidelity. Our approach provides a scalable framework for rapid magnetic structure prediction and opens a route to data-driven discovery of magnetic materials.

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