arXiv subjects

Daniel Olds

Publications and source records attributed to Daniel Olds.

15 recordsLinked to original sources

arXiv2026

Anomalous Hall Response Induced by Correlated Disorder in the Breathing Kagome Lattice Mn$_{3}$Sn

Macroscopic transport tensors are generally constrained by the average crystallographic and magnetic symmetries of a material. In the kagome antiferromagnetic Weyl semimetals Mn$_{3+\delta}X$ ($X=$~Sn or Ge), previous studies showed that the anomalous Hall conductivity $\sigma_{yx}$ is forbidden by the average \hexsg{} structure and coplanar inverse-triangular magnetic order. Here we report that nearly stoichiometric Mn$_3$Sn nevertheless exhibits a finite $\sigma_{yx}$ with large hysteresis, together with enhanced $\sigma_{zx}$ and $\sigma_{yz}$, in the inverse-triangular phase below $T_{\mathrm{N1}}\approx 440~\mathrm{K}$, whereas all AHE components vanish in the amplitude-modulated conical phase below $T_{\mathrm{N2}}\approx 280~\mathrm{K}$. Total scattering and magnetic pair distribution function analysis reveal correlated orthorhombic distortions and noncoplanar Mn moments. First-principles calculations show that this coupled lattice-spin distortion activates the average symmetry forbidden $\sigma_{yx}$ within the inverse-triangular phase. Its disappearance below $T_{\mathrm{N2}}$ indicates that the correlated disorder must cooperate with a long-range inverse-triangular antiferromagnetic order capable of supporting Berry curvature. Our results establish correlated disorder as an active symmetry-breaking degree of freedom that enables topological transport inaccessible from the Bragg-average structure alone.

cond-mat.str-el↗

arXiv2026

Synthesis and Characterization of Compositionally Complex (Gd/Ho/Er/Dy)2Zr2O7 Thin Film Combinatorial Library

High-throughput synthesis and characterization of novel ceramic materials with improved thermomechanical properties and phase stability are needed to accelerate the discovery of next-generation thermal barrier materials. A combinatorial thin film material library of (GdDyHoEr)2Zr2O7 were created via combinatorial magnetron reactive sputtering with rare-earth/zirconium alloy targets. Structural, chemical, and thermal property characterization mapping across the four component composition space was performed and correlated with thermal transport measurements. Steady state thermoreflectance mapping identifies a pronounced minimum in thermal conductivity within the Dy/Gd-rich quadrant. This minimum does not coincide with either the equiatomic composition or the region predicted to exhibit maximum cation size disorder. Instead, it corresponds to the largest experimentally observed lattice parameter, despite deviating from Vegard-like chemical averaging, and is independent of grain size and whole-pattern microstrain. These observations suggest that the way the fluorite lattice accommodates compositional complexity, rather than cation size disorder alone, provides a more informative descriptor of thermal transport. Overall, this work establishes a high-throughput workflow for combinatorial thin-film synthesis and multimodal characterization, enabling the rapid identification of previously inaccessible structure-property relationships in compositionally complex ceramics.

cond-mat.mtrl-sci↗

arXiv2026

PowderLine: a programmatic powder diffraction analysis application

Whole-pattern fitting methods, such as Rietveld refinement, excel at extracting detailed structural, chemical, and microstructural information from powder diffraction data. Obtaining reliable results requires both considerable expertise and software-specific knowledge, and applying these methods at scale typically relies on custom scripts written for each application. High-throughput experiments and autonomous self-driving laboratories increasingly utilize powder diffraction analysis to proceed programmatically and to return structured, machine-readable results. Here, we introduce PowderLine, a Python application that encapsulates a complete refinement into a single declarative recipe, validates that recipe against a versioned schema, and executes it through refinement software to return structured results. The refinement recipe is an all-inclusive, machine-readable and -writable description of either Rietveld or single peak analysis that users, scripts, and automated agents can specify and run in the same way. As a result of PowderLine's composability, it naturally fits into interactive, scripted, and autonomous workflows alike.

cond-mat.mtrl-sci↗

arXiv2025

A modular framework for collaborative human-AI, multi-modal and multi-beamline synchrotron experiments

High-throughput materials discovery and studies of complex functional materials increasingly rely on multi-modal characterization performed at synchrotron light sources. However, measurements are typically done with no use of data until after an experiment, neglecting opportunities for data-driven insights to guide measurements. We developed a modular, open-source framework that incorporates artificial intelligence within the Bluesky control and data streaming infrastructure at NSLS-II, enabling real-time orchestration of multi-beamline, multi-modal experiments. AI agents perform on-the-fly reduction, clustering, Gaussian process modelling, and Bayesian optimization driven data acquisition, while users monitor agent behavior and visualize results live. Combinatorial libraries of the ternary Al-Ni-Pt system were spatially mapped by X-ray diffraction and X-ray absorption fine structure measurements at the PDF and BMM beamlines, respectively. Dynamic switching between AI-driven and conventional grid mapping strategies was achieved, demonstrating the flexible workflows possible through this framework. A digital twin constructed from a simulated Al-Li-Fe oxide dataset shows that AI-driven mapping strategies outperform conventional mapping as well as random sampling by prioritizing measurements that better resolve both phase boundaries and localized minority phases. This framework supports plug-and-play capabilities, and establishes a foundation for routine multi-modal, AI-assisted large-scale user-facility operations.

physics.app-ph↗

arXiv2024

Simultaneous development of antiferromagnetism and local symmetry breaking in a kagome magnet (Co$_{0.45}$Fe$_{0.55}$)Sn

CoSn and FeSn, two kagome-lattice metals, have recently attracted significant attention as hosts of electronic flat bands and emergent physical properties. However, current understandings of their physical properties are limited to the knowledge of the average crystal structure. Here, we report the Fe-doping induced co-emergence of the antiferromagentic (AFM) order and local symmetry breaking in (Co0.45Fe0.55)Sn. Rietveld analysis on the neutron and synchrotron x-ray diffraction data indicates A-type antiferromagnetic order with the moment pointing perpendicular to the kagome layers, associated with the anomaly in the MSn(1)2Sn(2)4 (M = Co/Fe) octahedral distortion and the lattice constant c. Reverse Monte Carlo (RMC) modeling of the synchrotron x-ray total scattering results captured the subtle local orthorhombic distortion involving off-axis displacements of Sn2. Our results indicate that the stable hexagonal lattice above TN becomes unstable once the A-type AFM order is formed below TN. We argue that the local symmetry breaking has a magnetic origin and is driven by the out-of-plane magnetic exchange coupling. Our study provides comprehensive information on the crystal structure in both long-range scale and local scale, unveiling unique coupling between AFM order, octahedral distortion, and hidden local symmetry breaking.

cond-mat.str-el↗

arXiv2024

Distinguishing Isotropic and Anisotropic Signals for X-ray Total Scattering using Machine Learning

Understanding structure-property relationships is essential for advancing technologies based on thin films. X-ray pair distribution function (PDF) analysis can access relevant atomic structure details spanning local-, mid-, and long-range order. While X-ray PDF has been adapted for thin films on amorphous substrates, measurements on single crystal substrates are necessary to accurately determine structure origins for some thin film materials, especially those for which the substrate changes the accessible structure and properties. However, when measuring films on single crystal substrates, high intensity anisotropic Bragg spots saturate 2D detector images, overshadowing the thin films' isotropic scattering signal. This renders previous data processing methods for films on amorphous substrates unsuitable for films on single crystal substrates. To address this measurement need, we developed IsoDAT2D, an innovative data processing approach using unsupervised machine learning algorithms. The program combines non-negative matrix factorization and hierarchical agglomerative clustering to separate thin film and single crystal substrate X-ray scattering signals. We use SimDAT2D, a program we developed to generate synthetic thin film data, to validate IsoDAT2D. We also use IsoDAT2D to isolate X-ray total scattering signal from a thin film on a single crystal substrate. The resulting PDF data are compared to similar data processed using previous methods, demonstrating superior performance relative to substrate subtraction with a single crystal substrate and similar performance to substrate subtraction from an amorphous substrate. With IsoDAT2D, there are new opportunities to expand PDF to a wider variety of thin films, including those on single crystal substrates, with which new structure-property relationships can be elucidated to enable fundamental understanding and technological advances.

cond-mat.mtrl-sci↗

arXiv2024

X-ray and molecular dynamics study of the temperature-dependent structure of molten NaF-ZrF4

The local atomic structure of NaF-ZrF$_4$ (53-47 mol%) molten system and its evolution with temperature are examined with x-ray scattering measurements and compared with $ab-initio$ and Neural Network-based molecular dynamics (NNMD) simulations in the temperature range 515-700 {\deg}C. The machine-learning enhanced NNMD calculations offer improved efficiency while maintaining accuracy at higher distances compared to ab-initio calculations. Looking at the evolution of the Pair Distribution Function with increasing temperature, a fundamental change in the liquid structure within the selected temperature range, accompanied by a slight decrease in overall correlation is revealed. NNMD calculations indicate the co-existence of three different fluorozirconate complexes: [ZrF$_6$]$^{2-}$, [ZrF$_7$]$^{3-}$, and [ZrF$_8$]$^{4-}$, with a temperature-dependent shift in the dominant coordination state towards a 6-coordinated Zr ion at 700{\deg}C. The study also highlights the metastability of different coordination structures, with frequent interconversions between 6 and 7 coordinate states for the fluorozirconate complex from 525 {\deg}C to 700 {\deg}C. Analysis of the Zr-F-Zr angular distribution function reveals the presence of both $"$edge-sharing$"$ and $"$corner-sharing$"$ fluorozirconate complexes with specific bond angles and distances in accord with previous studies, while the next-nearest neighbor cation-cation correlations demonstrate a clear preference for unlike cations as nearest-neighbor pairs, emphasizing non-random arrangement. These findings contribute to a comprehensive understanding of the complex local structure of the molten salt, providing insights into temperature-dependent preferences and correlations within the molten system.

cond-mat.mtrl-sci↗

arXiv2023

Alloying Effects on the Microstructure and Properties of Laser Additively Manufactured Tungsten Materials

A large body of literature within the additive manufacturing (AM) community has focused on successfully creating stable tungsten (W) microstructures due to significant interest in its application for extreme environments. However, solidification cracking and additional embrittling features at grain boundaries have resulted in poorly performing microstructures, stymying the application of AM as a manufacturing technique for W. Several alloying strategies, such as ceramic particles and ductile elements, have emerged with the promise to eliminate solidification cracking while simultaneously enhancing stability against recrystallization. In this work, we provide new insights regarding the defects and microstructural features that result from the introduction of ZrC for grain refinement and NiFe as a ductile reinforcement phase - in addition to the resulting thermophysical and mechanical properties. ZrC is shown to promote microstructural stability with increased hardness due to the formation of ZrO2 dispersoids. Conversely, NiFe forms into micron-scale FCC phase regions within a BCC W matrix, producing enhanced toughness relative to pure AM W. A combination of these effects is realized in the WNiFe+ZrC system and demonstrates that complex chemical environments coupled with the tuning of AM microstructures provides an effective pathway for enabling laser AM W materials with enhanced stability and performance.

cond-mat.mtrl-sci↗

arXiv2023

Emulating Expert Insight: A Robust Strategy for Optimal Experimental Design

The challenge of optimal design of experiments (DOE) pervades materials science, physics, chemistry, and biology. Bayesian optimization has been used to address this challenge in vast sample spaces, although it requires framing experimental campaigns through the lens of maximizing some observable. This framing is insufficient for epistemic research goals that seek to comprehensively analyze a sample space, without an explicit scalar objective (e.g., the characterization of a wafer or sample library). In this work, we propose a flexible formulation of scientific value that recasts a dataset of input conditions and higher-dimensional observable data into a continuous, scalar metric. Intuitively, the scientific value function measures where observables change significantly, emulating the perspective of experts driving an experiment, and can be used in collaborative analysis tools or as an objective for optimization techniques. We demonstrate this technique by exploring simulated phase boundaries from different observables, autonomously driving a variable temperature measurement of a ferroelectric material, and providing feedback from a nanoparticle synthesis campaign. The method is seamlessly compatible with existing optimization tools, can be extended to multi-modal and multi-fidelity experiments, and can integrate existing models of an experimental system. Because of its flexibility, it can be deployed in a range of experimental settings for autonomous or accelerated experiments.

cond-mat.mtrl-sci↗

arXiv2023

Self-driving Multimodal Studies at User Facilities

Multimodal characterization is commonly required for understanding materials. User facilities possess the infrastructure to perform these measurements, albeit in serial over days to months. In this paper, we describe a unified multimodal measurement of a single sample library at distant instruments, driven by a concert of distributed agents that use analysis from each modality to inform the direction of the other in real time. Powered by the Bluesky project at the National Synchrotron Light Source II, this experiment is a world's first for beamline science, and provides a blueprint for future approaches to multimodal and multifidelity experiments at user facilities.

cond-mat.mtrl-sci↗

arXiv2022

Local structure and its implications for the relaxor ferroelectric Cd$_2$Nb$_2$O$_7$

The relaxor ferroelectric transition in Cd$_2$Nb$_2$O$_7$ is thought to be described by the unusual condensation of two $\Gamma$-centered phonon modes, $\Gamma_4^-$ and $\Gamma_5^-$. However, their respective roles have proven to be ambiguous, with disagreement between $\textit{ab initio}$ studies, which favor $\Gamma_4^-$ as the primary mode, and global crystal refinements, which point to $\Gamma_5^-$ instead. Here, we resolve this issue by demonstrating from x-ray pair distribution function measurements that locally, $\Gamma_4^-$ dominates, but globally, $\Gamma_5^-$ dominates. This behavior is consistent with the near degeneracy of the energy surfaces associated with these two distortion modes found in our own $\textit{ab initio}$ simulations. Our first-principles calculations also show that these energy surfaces are almost isotropic, providing an explanation for the numerous structural transitions found in Cd$_2$Nb$_2$O$_7$, as well as its relaxor behavior. Our results point to several candidate descriptions of the local structure, some of which demonstrate two-in/two-out behavior for Nb displacements within a given Nb tetrahedron. Although this suggests the possibility of a charge analog of spin ice in Cd$_2$Nb$_2$O$_7$, our results are more consistent with a Heisenberg-like description for dipolar fluctuations rather than an Ising one. We hope this encourages future experimental investigations of the Nb and Cd dipolar fluctuations, along with their associated mode dynamics.

cond-mat.mtrl-sci↗

arXiv2022

Machine learning enabling high-throughput and remote operations at large-scale user facilities

Imaging, scattering, and spectroscopy are fundamental in understanding and discovering new functional materials. Contemporary innovations in automation and experimental techniques have led to these measurements being performed much faster and with higher resolution, thus producing vast amounts of data for analysis. These innovations are particularly pronounced at user facilities and synchrotron light sources. Machine learning (ML) methods are regularly developed to process and interpret large datasets in real-time with measurements. However, there remain conceptual barriers to entry for the facility general user community, whom often lack expertise in ML, and technical barriers for deploying ML models. Herein, we demonstrate a variety of archetypal ML models for on-the-fly analysis at multiple beamlines at the National Synchrotron Light Source II (NSLS-II). We describe these examples instructively, with a focus on integrating the models into existing experimental workflows, such that the reader can easily include their own ML techniques into experiments at NSLS-II or facilities with a common infrastructure. The framework presented here shows how with little effort, diverse ML models operate in conjunction with feedback loops via integration into the existing Bluesky Suite for experimental orchestration and data management.

cs.LG↗

arXiv2021

Deep learning for visualization and novelty detection in large X-ray diffraction datasets

We apply variational autoencoders (VAE) to X-ray diffraction (XRD) data analysis on both simulated and experimental thin-film data. We show that crystal structure representations learned by a VAE reveal latent information, such as the structural similarity of textured diffraction patterns. While other artificial intelligence (AI) agents are effective at classifying XRD data into known phases, a similarly conditioned VAE is uniquely effective at knowing what it does not know, rapidly identifying novel phases and mixtures. These capabilities demonstrate that a VAE is a valuable AI agent for materials discovery and understanding XRD measurements both on-the-fly and during post hoc analysis.

cond-mat.mtrl-sci↗

arXiv2021

Constrained non-negative matrix factorization enabling real-time insights of $\textit{in situ}$ and high-throughput experiments

Non-negative Matrix Factorization (NMF) methods offer an appealing unsupervised learning method for real-time analysis of streaming spectral data in time-sensitive data collection, such as $\textit{in situ}$ characterization of materials. However, canonical NMF methods are optimized to reconstruct a full dataset as closely as possible, with no underlying requirement that the reconstruction produces components or weights representative of the true physical processes. In this work, we demonstrate how constraining NMF weights or components, provided as known or assumed priors, can provide significant improvement in revealing true underlying phenomena. We present a PyTorch based method for efficiently applying constrained NMF and demonstrate this on several synthetic examples. When applied to streaming experimentally measured spectral data, an expert researcher-in-the-loop can provide and dynamically adjust the constraints. This set of interactive priors to the NMF model can, for example, contain known or identified independent components, as well as functional expectations about the mixing of components. We demonstrate this application on measured X-ray diffraction and pair distribution function data from $\textit{in situ}$ beamline experiments. Details of the method are described, and general guidance provided to employ constrained NMF in extraction of critical information and insights during $\textit{in situ}$ and high-throughput experiments.

physics.app-ph↗

arXiv2021

Crystallography companion agent for high-throughput materials discovery

The discovery of new structural and functional materials is driven by phase identification, often using X-ray diffraction (XRD). Automation has accelerated the rate of XRD measurements, greatly outpacing XRD analysis techniques that remain manual, time-consuming, error-prone, and impossible to scale. With the advent of autonomous robotic scientists or self-driving labs, contemporary techniques prohibit the integration of XRD. Here, we describe a computer program for the autonomous characterization of XRD data, driven by artificial intelligence (AI), for the discovery of new materials. Starting from structural databases, we train an ensemble model using a physically accurate synthetic dataset, which output probabilistic classifications -- rather than absolutes -- to overcome the overconfidence in traditional neural networks. This AI agent behaves as a companion to the researcher, improving accuracy and offering significant time savings. It was demonstrated on a diverse set of organic and inorganic materials characterization challenges. This innovation is directly applicable to inverse design approaches, robotic discovery systems, and can be immediately considered for other forms of characterization such as spectroscopy and the pair distribution function.

cond-mat.mtrl-sci↗