arXiv subjects

Simon J. L. Billinge

Publications and source records attributed to Simon J. L. Billinge.

At least 19 recordsLinked to original sources

arXiv2026

Local Structure and Dynamics of Three-Dimensional Covalent Organic Frameworks

Resolving and controlling the local dynamical properties of covalent organic frameworks (COFs) remains a central challenge, particularly when assembled from large, flexible building units. Here, we combine synchrotron X-ray pair distribution function (PDF) analyses with machine learning-accelerated molecular dynamics (MD) simulations to resolve the local structure and dynamics of two three-dimensional imine-linked COFs, COF-682 {[(DHP)(TAM)]$_{imine}$}, assembled from 6,13-dihydropentacene (DHP) and tetrakis(4-aminophenyl)methane (TAM), and COF-612 {[(HBC-LA$_{12}$)(HAPT)$_2$]$_{imine}$}, assembled from nanographene dodecabenzaldehyde hexakis{[3,5-bis($p$-formylphenyl)-4,6-dimethoxyphenyl]}hexabenzocoronene (HBC-LA$_{12}$) and 2,3,6,7,14,15-hexa(4-aminophenyl)triptycene (HAPT). Validated against the experimental PDFs through ensemble-averaged calculations, the simulations show that the exposed $π$-surface and V-shaped geometry of the DHP linker endow COF-682 with enhanced local flexibility through face-to-face and offset $π$-stacking interactions differing in both their average interplanar separation and their ring-plane tilt angle. In contrast, the extended nanographene linker rigidifies COF-612 by maintaining the planarity of its fused cores, while the linker pendant aryl rings equip both COFs with enhanced librational ability. The simulations further provide quantitative measures of the translational and reorientational mobility of the linkers, revealing how local COF dynamics may be tuned by balancing non-covalent interactions and different degrees of aromatic rigidity. The PDF-MD experimental-computational approach holds promise as a general method beyond conventional crystallography to gain insights into the local properties of COFs with the aim of directing their dynamic function.

cond-mat.mtrl-sci↗

arXiv2026

ERAF4XRD: A multimodal agentic framework for constructing validated experimental X-ray diffraction databases from scientific literature

The scientific literature contains decades of experimental measurements that remain difficult to access as structured data for modern AI and data-driven research. Much of this information is distributed across figures, captions, text, and tables, requiring experimental data and their context to be identified, connected, and verified before they can be reused. Here we introduce ERAF4XRD (Experiment Reader Agentic Framework for X-Ray Diffraction), a fully automated multimodal (i.e., image and text), multi-agent framework that reconstructs validated X-ray diffraction (XRD) records from scientific publications. ERAF4XRD downloads and screens documents, identifies XRD figures, extracts and links metadata to the corresponding experimental data, and validates outputs against source evidence using an independent validation agent. On a manually curated benchmark of 273 scientific publications containing 3,150 candidate figures, ERAF4XRD achieved up to 98.7% accuracy for XRD figure identification and generated 1,400 metadata values across 22 fields. Independent manual assessment of the final validated records yielded 98.5% precision and 90.7% recall, with no unsupported metadata observed among 443 evaluated fields. By moving beyond information extraction to the reconstruction and validation of linked experimental records, ERAF4XRD establishes an automated approach for transforming published scientific information into machine-readable experimental datasets for AI and data-driven science.

cond-mat.mtrl-sci↗

arXiv2026

CbLDM: A Diffusion Model for recovering nanostructure from atomic pair distribution function

The nanostructure inverse problem is an attractive problem that helps researchers to understand the relationship between the properties and the structure of nanomaterials. This study focuses on the problem of recovering the model system of monometallic nanoparticles (MMNPs) from their pair distribution function (PDF) and regards it as a highly ill-posed conditional generation task. This study proposes a Condition-based Latent Diffusion Model (CbLDM) as a feasible solution to this problem. This model demonstrates an acceleration approach within the framework of a latent diffusion model by using conditional priors to estimate the conditional posterior distribution, which is an approximate distribution of p(z|x). In addition, this study uses Laplacian matrix instead of distance matrix to recover the nanostructure, which helps to improve stability. Our study demonstrates that a latent diffusion model with a conditional prior can generate nanostructures that are consistent with PDF observations and physically meaningful, thereby laying the groundwork for subsequent more complex inverse problems.

cs.LG↗

arXiv2026

diffpy.morph: Python tools for model independent comparisons between sets of 1D functions

diffpy$.$morph addresses a need to gain scientific insights from 1D scientific spectra in model independent ways. A powerful approach for this is to take differences between pairs of spectra and look for meaningful changes that might indicate underlying chemical, structural, or other modifications. The challenge is that the difference curve may contain uninteresting differences such as experimental inconsistencies and benign physical changes such as the effects of thermal expansion. diffpy$.$morph allows researchers to apply simple transformations, or "morphs", to one of the datasets to remove the unwanted differences revealing, when they are present, non-trivial differences. diffpy$.$morph is an open-source Python package available on the Python Package Index and conda-forge. Here, we describe its functionality and apply it to solve a range of experimental challenges on diffraction and PDF data from x-rays and neutrons, though we note that it may be applied to any 1D function in principle.

physics.comp-ph↗

arXiv2026

A continuous symmetry breaking measure for finite clusters using Jensen-Shannon divergence

A quantitative measure of symmetry breaking is introduced that allows the quantification of which symmetries are most strongly broken due to the introduction of some kind of defect in a perfect structure. The method uses a statistical approach based on the Jensen-Shannon divergence. The measure is calculated by comparing the transformed atomic density function with its original. Software code is presented that carries the calculations out numerically using Monte Carlo methods. The behavior of this symmetry breaking measure is tested for various cases including finite size crystallites (where the surfaces break the crystallographic symmetry), atomic displacements from high symmetry positions, and collective motions of atoms due to rotations of rigid octahedra. The approach provides a powerful tool for assessing local symmetry breaking and offers new insights that can help researchers understand how different structural distortions affect different symmetry operations.

cond-mat.mtrl-sci↗

arXiv2025

Representation of Inorganic Synthesis Reactions and Prediction: Graphical Framework and Datasets

While machine learning has enabled the rapid prediction of inorganic materials with novel properties, the challenge of determining how to synthesize these materials remains largely unsolved. Previous work has largely focused on predicting precursors or reaction conditions, but only rarely on full synthesis pathways. We introduce the ActionGraph, a directed acyclic graph framework that encodes both the chemical and procedural structure, in terms of synthesis operations, of inorganic synthesis reactions. Using 13,017 text-mined solid-state synthesis reactions from the Materials Project, we show that incorporating PCA-reduced ActionGraph adjacency matrices into a $k$-nearest neighbors retrieval model significantly improves synthesis pathway prediction. While the ActionGraph framework only results in a 1.34% and 2.76% increase in precursor and operation F1 scores (average over varying numbers of PCA components) respectively, the operation length matching accuracy rises 3.4 times (from 15.8% to 53.3%). We observe an interesting trade-off where precursor prediction performance peaks at 10-11 PCA components while operation prediction continues improving up to 30 components. This suggests composition information dominates precursor selection while structural information is critical for operation sequencing. Overall, the ActionGraph framework demonstrates strong potential, and with further adoption, its full range of benefits can be effectively realized.

cond-mat.mtrl-sci↗

arXiv2025

An Absorption Correction for Reliable Pair-Distribution Functions from Low Energy X-ray Sources

This paper explores the development and testing of a simple absorption correction model for processing x-ray powder diffraction data from Debye-Scherrer geometry laboratory x-ray experiments. This may be used as a pre-processing step before using PDFgetX3 to obtain reliable pair distribution functions (PDFs). The correction was found to depend only on muD, the product of the x-ray attenuation coefficient and capillary diameter. Various experimental and theoretical methods for estimating muD were explored, and the most appropriate muD values for correction were identified for different capillary diameters and x-ray beam sizes. We identify operational ranges of muD where reasonable signal to noise is possible after correction. A user-friendly software package, diffpy.labpdfproc, is presented that can help estimate muD and perform absorption corrections, with a rapid calculation for efficient processing.

cond-mat.mtrl-sci↗

arXiv2025

Charge Density Wave and Ferromagnetism in Intercalated CrSBr

In materials with one-dimensional electronic bands, electron-electron interactions can produce intriguing quantum phenomena, including spin-charge separation and charge density waves (CDW). Most of these systems, however, are non-magnetic, motivating a search for anisotropic materials where the coupling of charge and spin may affect emergent quantum states. Here, chemical intercalation of the van der Waals magnetic semiconductor CrSBr yields $Li_{0.17(2)} (tetrahydrofuran)_{0.26(3)} CrSBr$, which possess an electronically driven quasi-1D CDW with an onset temperature above room temperature. Concurrently, electron doping increases the magnetic ordering temperature from 132 K to 200 K and switches its interlayer magnetic coupling from antiferromagnetic to ferromagnetic. The spin-polarized nature of the anisotropic bands that give rise to this CDW enforces an intrinsic coupling of charge and spin. The coexistence and interplay of ferromagnetism and charge modulation in this exfoliatable material provides a promising platform for studying tunable quantum phenomena across a range of temperatures and thicknesses.

cond-mat.mtrl-sci↗

arXiv2025

Interpretable Multimodal Machine Learning Analysis of X-ray Absorption Near-Edge Spectra and Pair Distribution Functions

We used interpretable machine learning to combine information from multiple heterogeneous spectra: X-ray absorption near-edge spectra (XANES) and atomic pair distribution functions (PDFs) to extract local structural and chemical environments of transition metal cations in oxides. Random forest models were trained on simulated XANES, PDF, and both combined to extract oxidation state, coordination number, and mean nearest-neighbor bond length. XANES-only models generally outperformed PDF-only models, even for structural tasks, although using the metal's differential PDFs (dPDFs) instead of total PDFs narrowed this gap. When combined with PDFs, information from XANES often dominates the prediction. Our results demonstrate that XANES contain rich structural information and highlight the utility of species-specificity. This interpretable, multimodal approach is quick to implement with suitable databases and offers valuable insights into the relative strengths of different modalities, guiding researchers in experiment design and identifying when combining complementary techniques adds meaningful information to a scientific investigation.

cond-mat.mtrl-sci↗

arXiv2025

Testing Protocols for Obtaining Reliable PDFs from Laboratory x-ray Sources Using PDFgetX3

In this work, we explored data acquisition protocols and improved data reduction protocols using PDFgetX3 to obtain reliable data for atomic pair distribution function (PDF) analysis from a laboratory-based Mo x-ray source. A variable counting scheme is described that preferentially counts in the high-angle region of the diffraction pattern. The effects on the resulting PDF are studied by varying the overall count time, the use of Soller slits, and limiting the out-of-plane divergence of the incident beam. The protocols are tested using an amorphous silica and a quartz sample. We also present a modification to the current PDFgetX3 data corrections to take care of sample absorption, which was previously neglected in the use of that program for high-energy synchrotron x-ray data. We show that, despite limitations in the Q-range and flux of laboratory instruments, reasonable data for PDF model fits may be obtained using the best protocols in a few hours of counting.

cond-mat.mtrl-sci↗

arXiv2024

Resolving Length Scale Dependent Transient Disorder Through an Ultrafast Phase Transition

Material functionality can be strongly determined by structure extending only over nanoscale distances. The pair distribution function presents an opportunity to shift structural studies beyond idealized crystal models and investigate structure over varying length scales. Applying this method with ultrafast time resolution has the potential to similarly disrupt the study of structural dynamics and phase transitions. Here, we demonstrate such a measurement of CuIr$_{2}$S$_{4}$ optically pumped from its low temperature Ir-dimerized phase. Dimers are optically suppressed without spatial correlation, generating a structure whose level of disorder depends strongly on length scale. The re-development of structural ordering over tens of picoseconds is directly tracked over both space and time as a transient state is approached. This measurement demonstrates both the crucial role of local structure and disorder in non-equilibrium processes and the feasibility of accessing this information with state-of-the-art XFEL facilities.

cond-mat.mtrl-sci↗

arXiv2023

Operando pair distribution function analysis of nanocrystalline functional materials: the case of $\mathrm{TiO_{2}}$-bronze nanocrystals in Li-ion battery electrodes

Structural modelling of $operando$ pair distribution function (PDF) data of functional materials can be highly complex. To aid the understanding of complex operando PDF data, we here demonstrate a toolbox for PDF analysis. The tools include the structureMining, similarityMapping, nmfMapping apps available through the online service 'PDF in the cloud' (PDFitc, www.pdfitc.org), as well as noise-filtering using principal component analysis (PCA). The tools are applied to both ex situ and operando PDF data for 3 nm $\mathrm{TiO_{2}}$-bronze nanocrystals, which function as the active electrode material in a Li-ion battery. The tools enable structural modelling of the ex situ and operando PDF data, revealing two pristine $\mathrm{TiO_{2}}$ phases (bronze and anatase) and two lithiated $\mathrm{Li_{x}TiO_{2}}$ phases (lithiated versions of bronze and anatase), and the phase evolution during Galvanostatic cycling is characterized.

cond-mat.mtrl-sci↗

arXiv2023

Stretched Non-negative Matrix Factorization

An algorithm is described and tested that carries out a non negative matrix factorization (NMF) ignoring any stretching of the signal along the axis of the independent variable. This extended NMF model is called StretchedNMF. Variability in a set of signals due to this stretching is then ignored in the decomposition. This can be used, for example, to study sets of powder diffraction data collected at different temperatures where the materials are undergoing thermal expansion. It gives a more meaningful decomposition in this case where the component signals resemble signals from chemical components in the sample. The StretchedNMF model introduces a new variable, the stretching factor, to describe any expansion of the signal. To solve StretchedNMF, we discretize it and employ Block Coordinate Descent framework algorithms. The initial experimental results indicate that StretchedNMF model outperforms the conventional NMF for sets of data with such an expansion. A further enhancement to StretchedNMF for the case of powder diffraction data from crystalline materials called Sparse-StretchedNMF, which makes use of the sparsity of the powder diffraction signals, allows correct extractions even for very small stretches where StretchedNMF struggles. As well as demonstrating the model performance on simulated PXRD patterns and atomic pair distribution functions (PDFs), it also proved successful when applied to real data taken from an in situ chemical reaction experiment.

cond-mat.mtrl-sci↗

arXiv2023

Dynamic crystallography reveals spontaneous anisotropy in cubic GeTe

Cubic energy materials such as thermoelectrics or hybrid perovskite materials are often understood to be highly disordered. In GeTe and related IV-VI compounds, this is thought to provide the low thermal conductivities needed for thermoelectric applications. Since conventional crystallography cannot distinguish between static disorder and atomic motions, we develop the energy-resolved variable-shutter pair distribution function technique. This collects structural snapshots with varying exposure times, on timescales relevant for atomic motions. In disagreement with previous interpretations, we find the time-averaged structure of GeTe to be crystalline at all temperatures, but with anisotropic anharmonic dynamics at higher temperatures that resemble static disorder at fast shutter speeds, with correlated ferroelectric fluctuations along the $<$100$>$c direction. We show that this anisotropy naturally emerges from a Ginzburg-Landau model that couples polarization fluctuations through long-range elastic interactions. By accessing time-dependent atomic correlations in energy materials, we resolve the long-standing disagreement between local and average structure probes, and show that spontaneous anisotropy is ubiquitous in cubic IV-VI materials.

cond-mat.mtrl-sci↗

arXiv2022

DIFFPY.MPDF: Open-source software for magnetic pair distribution function analysis

The open-source python package diffpy.mpdf, part of the DiffPy suite for diffraction and pair distribution function analysis, provides a user-friendly approach for performing magnetic pair distribution function (mPDF) analysis. The package builds on existing libraries in the DiffPy suite to allow users to create models of magnetic structures and calculate corresponding one-dimensional and three-dimensional mPDF patterns. diffpy.mpdf can be used to perform fits to mPDF data either in isolation or in combination with atomic PDF data for joint refinements of the atomic and magnetic structure. Examples are given using MnO and MnTe as representative antiferromagnetic compounds and MnSb as a representative ferromagnet.

cond-mat.mtrl-sci↗

arXiv2022

Mapping Structural Heterogeneity at the Nanoscale with Scanning Nano-structure Electron Microscopy (SNEM)

Here we explore the use of scanning electron diffraction coupled with electron atomic pair distribution function analysis (ePDF) to understand the local order as a function of position in a complex multicomponent system, a hot rolled, Ni-encapsulated, Zr$_{65}$Cu$_{17.5}$Ni$_{10}$Al$_{7.5}$ bulk metallic glass (BMG), with a spatial resolution of 3 nm. We show that it is possible to gain insight into the chemistry and chemical clustering/ordering tendency in different regions of the sample, including in the vicinity of nano-scale crystallites that are identified from virtual dark field images and in heavily deformed regions at the edge of the BMG. In addition to simpler analysis, unsupervised machine learning was used to extract partial PDFs from the material, modeled as a quasi-binary alloy, and map them in space. These maps allowed key insights not only into the local average composition, as validated by EELS, but also a unique insight into chemical short-range ordering tendencies in different regions of the sample during formation. The experiments are straightforward and rapid and, unlike spectroscopic measurements, don't require energy filters on the instrument. We spatially map different quantities of interest (QoI's), defined as scalars that can be computed directly from positions and widths of ePDF peaks or parameters refined from fits to the patterns. We developed a flexible and rapid data reduction and analysis software framework that allows experimenters to rapidly explore images of the sample on the basis of different QoI's. The power and flexibility of this approach are explored and described in detail. Because of the fact that we are getting spatially resolved images of the nanoscale structure obtained from ePDFs we call this approach scanning nano-structure electron microscopy (SNEM), and we believe that it will be powerful and useful extension of current 4D-STEM methods.

cond-mat.mtrl-sci↗

arXiv2022

Towards a machine-readable literature: finding relevant papers based on an uploaded powder diffraction pattern

We investigate a prototype application for machine-readable literature. The program is called "pyDataRecognition" and serves as an example of a data-driven literature search, where the literature search query is an experimental data-set provided by the user. The user uploads a powder pattern together with the radiation wavelength. The program compares the user data to a database of existing powder patterns associated with published papers and produces a rank ordered according to their similarity score. The program returns the digital object identifier (doi) and full reference of top ranked papers together with a stack plot of the user data alongside the top five database entries. The paper describes the approach and explores successes and challenges.

cond-mat.mtrl-sci↗

arXiv2022

Robustness test of the spacegroupMining model for determining space groups from atomic pair distribution function data

Machine learning models based on convolutional neural networks have been used for predicting space groups of crystal structures from their atomic pair distribution function (PDF). However, the PDFs used to train the model are calculated using a fixed set of parameters that reflect specific experimental conditions, and the accuracy of the model when given PDFs generated with different choices of these parameters is unknown. In this paper, we report that the results of the top-1 accuracy and top-6 accuracy are robust when applied to PDFs of different choices of experimental parameters $r_\text{max}$, $Q_\text{max}$, $Q_\text{damp}$ and atomic displacement parameters.

cond-mat.mtrl-sci↗