Collective motions in strongly interacting magnets involve many spins and are often described in terms of integer-spin excitations. However, in certain cases, the collective motion can behave as if these integer excitations break apart into smaller, particle-like entities with unusual properties. Such fractionalized excitations in quantum magnets are commonly associated either with topological order in two dimensions or with criticality in one dimension. It remains unclear how these distinct mechanisms are connected across a dimensional crossover. Here we investigate the Ti-based quantum antiferromagnet, Cs_8LiNa_3Ti_12F_48, in which Ti^3+ (3d^1, S=1/2) ions interact antiferromagnetically within distorted kagome planes. Our inelastic neutron scattering study on a single crystal reveals a frustrated network of weakly coupled spin-1/2 chainsaws, realizing a regime of dimensional frustration in which interchain couplings fail to establish coherent two-dimensional order. The magnetic excitation spectrum exhibits a strong continuum spanning the full measured momentum and energy phase space. In addition, the dynamic spin correlation function displays rod-like scattering in momentum space, indicating a quasi-one-dimensional nature of the magnetic correlations. These results point to fractionalized excitations with intrinsically directional character, demonstrating that signatures of one-dimensional criticality can persist within a two-dimensional lattice. Our findings establish anisotropic fractionalization as a distinct organizing principle for quantum-disordered states.

Recent advances in ab initio molecular dynamics (AIMD) have enabled precise simulations of vibrational dynamics in molecular systems; however, the high computational cost of AIMD limits its application to small-scale systems and short time spans. Machine learning interatomic potentials (MLIPs) offer a promising route to extend these simulations. Training of MLIPs, however, is usually nontrivial, especially when attempting to capture both the structural and vibrational dynamics of molecular systems. In this paper, we introduce a multi-stage workflow that combines AIMD data from diverse simulation packages to simultaneously train multiple deep-learning models such as DeePMD-kit, NequIP, and Allegro. The framework employs a genetic algorithm for hyperparameter optimization and utilizes inelastic neutron scattering spectra as an additional performance metric, ensuring both the static structure and dynamic behavior are accurately reproduced. This integrated approach not only enhances the reliability of MLIPs in capturing complex interatomic interactions but also paves the way for more predictive and efficient materials modeling.

NeuXtalViz (Neutron Single-Crystal Visualization) is a Python-based software package developed at Oak Ridge National Laboratory to provide interactive three-dimensional visualization and analysis tools for single-crystal neutron diffraction experiments. Built on the Mantid framework for data reduction, and leveraging PyVista and Matplotlib within a Python Qt environment, NeuXtalViz adopts a model-view-presenter architecture that separates the user interface from the core processing components. The software provides a unified interface for tasks central to single-crystal diffraction, including UB-matrix determination, experiment planning, visualization of normalized reciprocal-space volumes, and real-space crystal-structure calculations. It also integrates with widely used community tools and has been deployed on instrument and analysis servers, where it is now being adopted by instrument teams and users. By embedding advanced three-dimensional visualization directly into the experimental workflow, NeuXtalViz enhances the planning, execution, and analysis cycle for single-crystal neutron diffraction experiments, while providing a flexible framework for future development.

Weak lattice distortions can tune exchange pathways and magnetic interactions in square-lattice quantum magnets. K2V3O8 is a mixed valence (V4+/V5+) fresnoite oxide that exhibits strong spin-lattice coupling at low temperature. We combine single-crystal neutron diffraction (90 K) and laboratory X-ray diffraction (50 K) to solve the low-temperature structure as an orthorhombic (3 + 1)D incommensurately modulated phase in superspace group Cmm2(β,0,1/2)0s0 [No. 196]. What initially appeared as two independent modulation vectors, q1 = 0.3132(6)[110] + 1/2c* and q2 = 0.3132(6)[11̅0] + 1/2c*, are more naturally described as a single one-dimensional modulation wave q = 0.626(1)a* + 1/2c* in a C-centered orthorhombic lattice, related to the parent tetragonal cell by the transformation a + b, -a + b, c. Refinement with a 4-fold rotational twin inherited from the P4bm parent structure solves oxygen-dominated framework distortions and K+ displacements. A de Wolff section (t = 0.40) enables a symmetry-mode decomposition, identifying three dominant mm2 (C2v) modes: GM3 for framework tilt, A5 for interlayer shear, and Z5 for c-axis breathing. The mode-resolved structure provides a unified, symmetry-based explanation for reported low-temperature Raman and IR anomalies and clarifies the structural origin of the spin-lattice coupling in the S = 1/2 two-dimensional quantum spin compound.

Neutron time-of-flight (TOF) event data at the Spallation Neutron Source (SNS) provides a wealth of temporal information in multidimensional diffraction and parameter spaces. However, current analysis methods rely on sequential steps that can limit the potential for on-the-fly analysis of complex single-crystal neutron diffraction patterns and hinder the seamless integration of knowledge across different analysis techniques and workflows. To address this, we present an integrated framework for real-time single-crystal neutron diffraction data reduction and analysis. Our approach combines advanced AI/ML models with high-performance computing (HPC) at the Oak Ridge Leadership Computing Facility (OLCF). This integration enables seamless experiment steering and real-time decision-making, optimizing the use of valuable neutron beam time. Our primary focus is on TOPAZ, a high-resolution single-crystal TOF Laue diffractometer at SNS. We analyze live 4D spatiotemporal data (3D in Q-space with time as the fourth dimension) for precise, voxel-level prediction of neutron scattering patterns. The core of our method is a Temporal Fusion Transformer (TFT) model, a specialized deep neural network (DNN) tailored for multi-horizon forecasting tasks. This model, embedded within a Markovian stochastic framework, offers unprecedented accuracy and interpretability in predicting neutron scattering patterns at the voxel level across temporal 4D spaces. We showcase the feasibility of this approach using a hierarchical parallelization scheme on the OLCF Frontier supercomputer with a subset of neutron TOF event data from the TOPAZ beamline. Our results demonstrate the transformative potential of integrating AI/ML and HPC for real-time experiment steering and decision-making in multidimensional diffraction and parameter spaces. This work signifies a substantial leap in neutron scattering research, paving the way for greater automation and efficiency. Ultimately, this integrated method, aligning BES's neutron facilities with ASCR's HPC resources using AI/ML, is a crucial component of a future Integrated Research Infrastructure (IRI). We invite collaboration to explore opportunities further and redefine the future of neutron science together.

For the discovery of new materials in the field of energy storage, catalysis, and biological processes, molecular dynamics (MD) simulations are an indispensable computational tool. We can achieve highly accurate representations of the potential energy landscape of diverse molecular systems with ab-initio molecular dynamics (AIMD) simulations, but at the cost of high computational time which limits typical applications to 100s of atoms and time scales of ∼100ps. This is where machine learning potentials can be used to capture the underlying physics from first principles, where electrons are treated quantum mechanically, while still reaching long simulation times relatively cheaply. We introduce a workflow for the analysis of neutron scattering data that trains several deep-learning based many-body potentials and interatomic forces from ab-initio reference calculations. Further, to gauge the accuracy of such potentials with classical MD programs, we use the inelastic neutron scattering (INS) spectra as the performance metric. An INS spectra serves as one of the most stringent tests of theory (such as density functional theory), since the model has to predict not only the correct structure but also the correct vibrational dynamics. We use a genetic algorithm to optimize several hyperparameters used in this workflow. For different molecular samples, we successfully demonstrate that our workflow can replicate the experimental INS spectra.

At the debut of single crystal neutron diffraction over 80 years ago, it was a courageous undertaking to find diffracting and reflecting intensities in space with a single point detector. Since then, it has been a fantastic journey to watch the methodology progress and it’s impact on discovery of materials and their properties.Traditional Neutron Bragg intensity measurements integrating discrete “peak above background” counts meanwhile metamorphosed into high resolution diffraction space images that can be effectively combined into complete volumes of reciprocal space.The structure information between the discrete Bragg peaks in form of intensities from structure modulations that are on step or out of step with the overall long range ordered Bragg structure can be comprehensively visualized and analyzed.Analysis of materials properties stemming from short range order influences of structural defects, chemical disorder, positional disorder and other structure frustration manifestations in diffuse scattering can be visualized and most recently also analyzed.This might be a good time to draw attention to an emerging crystalline state at the threshold between fully long range ordered and amorphous or liquid, like plastic crystalline phases. Often, they are present over several degrees Kelvin before a crystal is fully long range ordered. In this state the structured diffuse scattering gives detailed insight of the next neighbor correlations and growing coordination shells. Long range order is forming in the plastic single crystal, which can also be observed.Data modeling makes use of 3D delta-PDF analysis, showing positive and negative correlations on a “model-free” basis. Recognizing the details that can be discovered in 3D delta-PDF analysis are just beginning to emerge.This is just one of the exciting developments that mark a transformation from discrete Bragg peak analysis to image processing and modeling which are equally powerful for monochromatic X-ray and polychromatic neutron wavelength resolved diffraction imaging.A portion of this research used resources at the Spallation Neutron Source, a DOE Office of Science User Facility operated by the Oak Ridge National Laboratory.

We introduce a computational framework that integrates artificial intelligence (AI), machine learning, and high-performance computing to enable real-time steering of neutron scattering experiments using an edge-to-exascale workflow. Focusing on time-of-flight neutron event data at the Spallation Neutron Source, our approach combines temporal processing of four-dimensional neutron event data with predictive modeling for multidimensional crystallography. At the core of this workflow is the Temporal Fusion Transformer model, which provides voxel-level precision in predicting 3D neutron scattering patterns. The system incorporates edge computing for rapid data preprocessing and exascale computing via the Frontier supercomputer for large-scale AI model training, enabling adaptive, data-driven decisions during experiments. This framework optimizes neutron beam time, improves experimental accuracy, and lays the foundation for automation in neutron scattering. Although real-time experiment steering is still in the proof-of-concept stage, the demonstrated potential of this system offers a substantial reduction in data processing time from hours to minutes via distributed training, and significant improvements in model accuracy, setting the stage for widespread adoption across neutron scattering facilities and more efficient exploration of complex material systems.

Anomalous X-ray diffraction (AXD) and neutron diffraction can be used to crystallographically distinguish between metals of similar electron density. Despite the use of AXD for structural characterization in mixed metal clusters, there are no benchmark studies evaluating the accuracy of AXD toward assessing elemental occupancy in molecules with comparisons with what is determined via neutron diffraction. We collected resonant diffraction data on several homo and heterometallic clusters and refined their anomalous scattering components to determine metal site occupancies. Theoretical resonant scattering terms for Fe0, Co0, and Zn0 were compared against experimental values, revealing theoretical values are ill-suited to serve as references for occupancy determination. The cluster featuring distinct cation and anion metal compositions [CoCp2*][(tbsL)Fe3(μ3-NAr)] was used to assess the accuracy of different f' references for occupancy determination (f'theoretical ± 15-17%; f'experimental ± 10%). This methodology was applied toward calculating the occupancy of three different clusters: (tbsL)Fe2Zn(py) (6), (tbsL)Fe2Zn(μ3-NAr)(py) (7), and [CoCp*2][(tbsL)Fe2Zn(μ3-NAr)] (8). The first two clusters maintain 100% Fe/Zn site isolation, whereas 8 showed metal mixing within the sites. The large crystal size of 8 enabled collection of neutron diffraction data which was compared against the results found with AXD. The ability of AXD to replicate the metal occupancies as determined by neutron diffraction supports the AXD occupancy methodology developed herein. Furthermore, the advantages innate to AXD (e.g., smaller crystal sizes, shorter collection times, and greater availability of synchrotron resources) versus neutron diffraction further support the need for its development as a standard technique.

Bridgmanite, the most abundant mineral in the lower mantle, can play an essential role in deep-Earth hydrogen storage and circulation processes. To better evaluate the hydrogen storage capacity and its substitution mechanism in bridgmanite occurring in nature, we have synthesized high-quality single-crystal bridgmanite with a composition of (Mg0.88Fe0.052+Fe0.053+Al0.03)(Si0.88Al0.11H0.01)O-3 at nearly water-saturated environments relevant to topmost lower mantle pressure and temperature conditions. The crystallographic site position of hydrogen in the synthetic (Fe,Al)-bearing bridgmanite is evaluated by a time-of-flight single-crystal neutron diffraction scheme, together with supporting evidence from polarized infrared spectroscopy. Analysis of the results shows that the primary hydrogen site has an OH bond direction nearly parallel to the crystallographic b axis of the orthorhombic bridgmanite lattice, where hydrogen is located along the line between two oxygen anions to form a straight geometry of covalent and hydrogen bonds. Our modeled results show that hydrogen is incorporated into the crystal structure via coupled substitution of Al3+ and H+ simultaneously exchanging for Si4+, which does not require any cation vacancy. The concentration of hydrogen evaluated by secondary-ion mass spectrometry and neutron diffraction is similar to 0.1 wt% H2O and consistent with each other, showing that neutron diffraction can be an alternative quantitative means for the characterization of trace amounts of hydrogen and its site occupancy in nominally anhydrous minerals.

BaNiO 3-type "hexagonal perovskite" materials are known to exhibit complex phase diagram

Decomposition of decafluorobiphenyl andanthracene providesa source of carbon, hydrogen, and fluorine during metal flux growthof an intermetallic. These species react with boron in a cerium/coppermelt, yielding crystals of Ce4B2C2F0.14H2.26. The siting and occupancies of fluorideand hydride interstitials were confirmed using single-crystal X-raydiffraction and neutron diffraction; F/H site mixing destroys magneticordering in the compound. Fluoride interstitials were introduced into an intermetallicstructureby carrying out metal flux growth in the presence of a fluorocarbon.Decafluorobiphenyl and anthracene were reacted with boron in a meltcomposed of cerium and copper, yielding crystals of Ce4B2C2F0.14H2.26, a fluorinatedanalogue of previously reported Ce4B2C2H2.42. The siting and occupancies of the fluoride andhydride interstitials were confirmed using both single-crystal X-raydiffraction and neutron diffraction. The fluoride mixes with the hydridein an octahedral interstitial site surrounded by cerium cations; additionalhydride ions occupy the tetrahedral sites. Magnetic susceptibilityshows a change from canted antiferromagnetic ordering reported forthe hydride to paramagnetic behavior upon fluorine substitution.

An atomic view of a main aqueous conformation of cyclosporine A (CycA), an important 11-amino-acid macrocyclic immunosuppressant, is reported. For decades, it has been a grand challenge to determine the conformation of free CycA in an aqueous-like solution given its poor water solubility. Using a combination of X-ray and single-crystal neutron diffraction, we unambiguously resolve a unique conformer (A1) with a novel cis-amide between residues 11 and 1 and two water ligands that stabilize hydrogen bond networks. NMR spectroscopy and titration experiments indicate that the novel conformer is as abundant as the closed conformer in 90/10 (v/v) methanol/water and is the main conformer at 10/90 methanol/water. Five other conformers were also detected in 90/10 methanol/water, one in slow exchange with A1, another one in slow exchange with the closed form and three minor ones, one of which contains two cis amides Abu2-Sar3 and MeBmt1-MeVal11. These conformers help better understand the wide spectrum of membrane permeability observed for CycA analogues and, to some extent, the binding of CycA to protein targets.

Powder and single crystal diffraction are analytical methods that have developed into widely used tools for material analysis on molecular and atomic level.Dependent on the sample symmetry, the material to be investigated and the question to answer, X-ray diffraction and increasingly also neutron diffraction are the investigative tools of choice due to their complementary sensitivity to heavy and light elements.Data collection with area detectors is more and more automated.Bragg diffraction intensities are collected highly efficiently and data reduction is streamlined.For single crystal structure analysis, raw diffraction intensities are reduced to an extracted structure factor amplitude for each measured Bragg reflection, and the reduced data set represents the composition and symmetry arrangement of the sample.The measured Bragg intensities are raw intensities and need to be corrected for sample absorption due to chemical composition and size, the experimental setup that might add extra scattering, for example glue used to secure the sample, or the detectors that might have variations in detection sensitivity and electronic noise.Those are just a few examples for sample and instrument specific corrections.Other corrections that are dependent on the radiation (X-rays or neutrons) and on monochromatic or polychromatic incident beams are Lorentz and polarization corrections, scaling and normalization.Being aware of the various corrections and when they are applied in the data reduction workflow can help to solve issues that surface during data analysis.Understanding the effects of various corrections are even more important, when relatively weak superlattice intensities or diffuse scattering are studied.This contribution aims to shed light on which corrections are applied to diffraction data, at which step of data reduction they are applied and they might impact the structure factor amplitudes and data analysis.

Significance While disorder is an inalienable characteristic of real crystalline materials, the capability of controlling various types of disorder often strongly influences our understanding of science and the advancement of technology. Magnetic spinel represents a class of materials with a pyrochlore-structured sublattice to potentially host three-dimensional spin frustration but is strongly influenced by the inversion disorder of two similarly sized cation species. While it remains challenging to experimentally differentiate these two characteristics, here we mitigate the disorder issue at the crystal growth stage. Our independent control of both stoichiometry and inversion disorder clarifies both magnetism and structure in a spinel oxide of interest for seven decades.

Emergent relativistic quasiparticles in Weyl semimetals are the source of exotic electronic properties such as surface Fermi arcs, the anomalous Hall effect and negative magnetoresistance, all observed in real materials. Whereas these phenomena highlight the effect of Weyl fermions on the electronic transport properties, less is known about what collective phenomena they may support. Here, we report a Weyl semimetal, NdAlSi, that offers an example. Using neutron diffraction, we found a long-wavelength helical magnetic order in NdAlSi, the periodicity of which is linked to the nesting vector between two topologically non-trivial Fermi pockets, which we characterize using density functional theory and quantum oscillation measurements. We further show the chiral transverse component of the spin structure is promoted by bond-oriented Dzyaloshinskii–Moriya interactions associated with Weyl exchange processes. Our work provides a rare example of Weyl fermions driving collective magnetism.