arXiv subjects

Frank M. Abel

Publications and source records attributed to Frank M. Abel.

8 recordsLinked to original sources

arXiv2026

Hybrid DiffractGPT-Rietveld Refinement Framework for Automated X-ray Diffraction Analysis

X-ray diffraction (XRD) is fundamental to structural materials characterization, yet transforming a raw powder pattern into a refined crystal structure still demands considerable domain expertise. We present AGAPI-XRD, a hybrid framework integrating DiffractGPT generative structure prediction, database pattern matching against JARVIS-DFT and COD, and automated Rietveld refinement and ALIGNN-FF relaxation through a unified API at https://atomgpt.org/xrd. First, we used the AGAPI-XRD pipeline to evaluate the crystal structure of a variety of minerals in the RRUFF database that were experimentally characterized using powder x-ray diffraction. Next, we benchmarked the lattice parameter prediction fidelity of the AGAPI-XRD pipeline using a subset of the Alexandria PBE-hull dataset and the subset of RRUFF minerals that have known lattice parameters. AGAPI-XRD returns valid lattice parameters for 79.7\% of the RRUFF benchmark minerals and for 94.8--98.1\% of the Alexandria subset, while identifying a candidate structure for 93.8\% of RRUFF minerals. For this benchmark, pattern matching delivers the highest accuracy for known phases, while DiffractGPT extends structure generation to complex materials absent from existing databases. Together, AGAPI-XRD advances accessible, end-to-end automated crystal structure determination from powder XRD data.

cond-mat.mtrl-sci↗

arXiv2026

RamanGPT: Bidirectional Mapping Between Crystal Structures and Raman Spectra with Graph Neural Networks and Generative Transformers

Raman spectroscopy is one of the most accessible vibrational probes in materials laboratories, but its forward problem (structure to spectrum) is bottlenecked by the cost of density functional perturbation theory, and its inverse problem (spectrum to structure) typically relies on retrieval against curated references. We introduce RamanGPT, a deep-learning framework that addresses both directions for crystalline inorganic materials. The forward model, an Atomistic Line Graph Neural Network (ALIGNN), is trained on the 5{,}099-material Computational Raman Database and predicts 200-bin spectra over 50-1000~cm$^{-1}$ with 42.5\% having a cosine similarity greater than or equal to 0.354 suggesting qualitative features of the target spectrum. The model also shows some qualitative agreement with the approximate features and appearance of similar relative intensity of the modes to an experimental measurement of metallic 1T VSe$_{2}$, a system absent from the training set. The inverse model fine-tunes a large language model via Quantized Low-Rank Adaptation on Raman-plus-formula prompts, recovering lattice parameters with mean absolute errors of 1.14-2.16~Å and reduced-formula consistency of 86.8\% on 508 held-out materials. A cosine-similarity matcher and an inverse$\rightarrow$relax$\rightarrow$forward consistency loop are deployed at https://atomgpt.org/raman.

cond-mat.mtrl-sci↗

arXiv2026

Magnetodynamics of short nanoparticle chains

In recent years, there has been increasing interest in the understanding and application of nanoparticle assemblies driven by external fields. Although these systems can exhibit marked transitions in behavior compared to non-interacting counterparts, it has often proven challenging to connect their dynamics with underlying physical mechanisms or even to verifiably establish their structure under realistic experimental conditions. We have studied colloidal iron oxide nanoparticles that assemble into ordered, few-particle linear chains under the influence of oscillating and pulsed magnetic fields. In this work, our goal has been to answer the following question: by what physical mechanisms does the magnetic switching of a linear chain evolve from the switching of its constituent particles? Cryo-TEM has been used to flash freeze and image the structures formed by oscillatory drive fields, and magnetic relaxometry has been used to extract the multiple time constants associated with magnetic switching of the short chains. Armed with the physical structure from microscopy and the field-dependent switching times from magnetic measurements, we have conducted extensive micromagnetic simulations, revealing probable physical mechanisms for each time constant regime spanning ~1 microsecond to 1 s in time. These types of magnetic nanomaterials have great potential for biomedical technologies, particularly magnetic particle imaging and hyperthermia, and rigorous elucidation of their physics will hasten their optimization.

cond-mat.mtrl-sci↗

arXiv2025

Machine Learning for Predicting Magnetization from X-ray Diffraction of Iron Oxide Nanoparticles Using Simple Physics-Based Data Generation

Automation and high-throughput characterization and synthesis for material development are becoming increasingly common; these approaches require machine learning (ML) tools to assess material properties, ideally based on a single measurement. Here, ML models are developed to predict magnetization from X-ray diffraction (XRD) for iron oxide nanoparticles. Our approach is to first develop a set of simulated data that links modulated XRD, based on a crystallographic information file (CIF), to a simple magnetic model to determine magnetization at a given magnetic field, thereby enabling us to train Random Forest and Gradient Boosting regression models on a large amount of simulated data. The models are validated by synthesizing iron oxide nanoparticles and measuring their crystal structure via XRD and room-temperature magnetization curves. In doing so, we can fine-tune both the training hyperparameters and the optimal size of the simulated datasets used to train the models. Through this optimization, the best models can achieve an $R^2$ greater than 0.9 for five experimental samples, used for tuning, for predicting the max magnetization (at 2.8 T) of the measurement. Lastly, we demonstrate reasonable predictions on the full magnetization vs. magnetic field curve, showing that the RF model excels at predicting the high magnetic field values, which is key for determining the success of an iron oxide nanoparticle synthesis for applications like magnetic particle imaging (MPI), thermal magnetic particle imaging (T-MPI), and hyperthermia.

cond-mat.mtrl-sci↗

arXiv2025

Anomalous low-field magnetoresistance in Fe$_3$Ga$_4$ single crystals

Fe$_3$Ga$_4$ possesses a helical spin spiral with a complex competition between ferromagnetic and antiferromagnetic ground states. This competition generates multiple metamagnetic transitions that are governed by both applied magnetic field and temperature. At intermediate temperatures between T$_1$ (68 K) and T$_2$ (360 K), the ferromagnetically aligned spins transition to an antiferromagnetic spin spiral. In this study, magnetoresistance (MR) measurements are performed on an aligned single crystal and compared to magnetization properties in order to gain insight on the unique alignment of the spins. The high-field MR is positive at low temperatures indicating cyclotronic behavior and negative at high temperature from electron-magnon scattering. Of particular significance is a large anomalous positive MR at low fields, possibly due to emergent spin fluctuations thus prompting further exploration of this multifaceted material.

cond-mat.mtrl-sci↗

arXiv2025

Non-Altermagnetic Origin of Exchange Bias Behaviors in Incoherent RuO$_2$/Fe Bilayer Heterostructures

Initially identified as a promising altermagnetic (AM) candidate, rutile RuO$_2$ has since become embroiled in controversy due to contradictory findings of modeling and measurements of the magnetic properties of bulk crystals and thin films. For example, despite observations of a bulk non-magnetic state using density functional theory, neutron scattering, and muon spin resonance measurements, patterned RuO$_2$ Hall bars and film heterostructures display magnetotransport signatures of magnetic ordering. Among the characteristics routinely cited as evidence for AM is the observation of exchange bias (EB) in an intimately contacted Fe-based ferromagnetic (FM) layer, which can arise due to interfacial coupling with a compensated antiferromagnet. Within this work, the origins of this EB coupling in Ru-capped RuO$_2$/Fe bilayers are investigated using polarized neutron diffraction, polarized neutron reflectometry, cross-sectional transmission electron microscopy, and super conducting quantum interference device measurements. These experiments reveal that the EB behavior is driven by the formation of an iron oxide interlayer containing Fe$_3$O$_4$ that undergoes a magnetic transition and pins interfacial moments within Fe at low temperature. These findings are confirmed by comparable measurements of Ni-based heterostructures, which do not display EB coupling, as well as magnetometry of additional Fe/Ru bilayers that display oxide-driven EB coupling despite the absence of the epitaxial RuO$_2$ layer. While these results do not directly refute the possibility of AM ordering in RuO$_2$ thin films, they reveal that EB, and related magnetotransport phenomena, cannot alone be considered evidence of this characteristic in the rutile structure due to interfacial chemical disorder.

cond-mat.mtrl-sci↗

arXiv2025

Probing Magnetic Properties of RuO$_{2}$ Heterostructures Through the Ferromagnetic Layer

RuO$_{2}$ has been proposed as the prototypical altermagnetic material. However, several reports have recently questioned its intrinsic magnetic ordering, leading to conflicting findings, especially in thin film heterostructures pointing to possible interface effects being convoluted with supposed antiferromagnetic/altermagnetic signatures. Here, extensive magnetometry measurements were performed on two independently grown thin film heterostructures of RuO$_{2}$ interfaced with either NiFe or Fe acting as the ferromagnetic layer. Below about 15 K, both samples exhibit exchange bias fields when cooled to approximately 2 K in a $+$1 T field, and a spin transitional feature is observed around 31 K. Magneto-Raman measurements on RuO$_{2}$ thin films only reveal a magnon mode when there is a NiFe layer, suggesting that RuO$_{2}$ does not intrinsically possess long range magnetic ordering.. When in contact with a ferromagnet, RuO$_2$ displays effects that could be ascribed to antiferromagnetism. However, the lack of intrinsic magnon modes points toward possible diffusion between the layers or spin disorder at the interface as seen by density functional theory (DFT) calculations.

cond-mat.mtrl-sci↗

arXiv2025

Quantum Monte Carlo and density functional theory study of strain and magnetism in 2D 1T-VSe$_2$ with charge density wave states

Two-dimensional (2D) 1T-VSe$_2$ has prompted significant interest due to the discrepancies regarding alleged ferromagnetism (FM) at room temperature, charge density wave (CDW) states and the interplay between the two. We employed a combined Diffusion Monte Carlo (DMC) and density functional theory (DFT) approach to accurately investigate the magnetic properties, CDW states, and their response to strain in monolayer 1T-VSe$_2$. Our calculations show the delicate competition between various phases, revealing critical insights into the relationship between their energetic and structural properties. We performed classical Monte Carlo simulations informed by our DMC and DFT results, and found the magnetic transition temperature ($T_c$) of the undistorted (non-CDW) FM phase to be 228 K and the distorted (CDW) phase to be 68 K. Additionally, we studied the response of biaxial strain on the energetic stability and magnetic properties of various phases of 2D 1T-VSe$_2$ and found that small amounts of strain can increase the $T_c$, suggesting a promising route for engineering and enhancing magnetic behavior. Finally, we synthesized 1T-VSe$_2$ and performed Raman spectroscopy measurements, which were in close agreement with our calculated results, validating our computational approach. Our work emphasizes the role of highly accurate DMC methods in advancing the understanding of monolayer 1T-VSe$_2$ and provides a robust framework for future studies of 2D magnetic materials.

cond-mat.mtrl-sci↗