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Open Access
Abstract: Total scattering models are essential for characterizing the structure and disorder of nanoscale materials. The Debye scattering equation (DSE) provides a rigorous route to elastic total scattering, but its direct evaluation is computationally demanding because pairwise contributions must be accumulated at every scattering vector, whereas common acceleration strategies based on binned pair-distance distributions or gridded fast Fourier transforms can introduce discretization and aliasing artifacts that compromise diffuse-scattering accuracy. Here, we present AES-Debye, an accuracy-preserving DSE framework that aggregates pair distances into a pair distribution function (PDF) using corrected bin centers and numerically robust accumulation to suppress discretization and summation errors. A data-locality-aware parallel design enables efficient execution on CPUs and GPUs. We demonstrate strong scalability by computing a high-resolution total scattering profile for a system of 90 million atoms, (0.1 µm)3, in minutes on a distributed-memory CPU platform. These capabilities extend accurate elastic total scattering calculations to large complex systems while simultaneously providing high-resolution PDFs for downstream structural analysis.
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Oct 2026
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Open Access
Abstract: Non-destructive evaluation (NDE) is used to detect and characterise defects in safety-critical components. This paper focuses on manual pulse-echo ultrasonic testing applied to the sizing of surface-breaking crack-like defects. In the field, practitioners often use A-scans from single element, pulse-echo ultrasonic testing. Machine learning, and in particular neural networks, have huge potential for applications to NDE-type problems due to their ability to recognise patterns from signals. Accurate predictions require large, labelled databases yet there is a paucity of such ultrasonic data for surface-breaking thermally fatigued cracks. To address this deficit, this work demonstrates that a database composed entirely of simulated, synthetic A-scans provides promising predictions on measured data. Crack height predictions with a mean absolute error of around 0.124 mm are achieved for measured data (on cracks between 0.5 mm and 4 mm). When tested on simulated A-scans, both height and tilt angle predictions are extremely accurate (to within 0.035 mm and 0.41°, respectively). To achieve such accuracy the in-situ inspection techniques were mimicked, with particular attention paid to the characteristics of the thermally fatigued crack species, and the single element transducer and its input signal. The details of the finite element simulations that generated the database are presented here. The work outlined in this paper shows that A-scans can be used to size defects using neural networks trained entirely on synthetic data, informed by experimental measurements, with potential applications for improving the efficiency of sizing cracks in-situ.
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Oct 2026
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I14-Hard X-ray Nanoprobe
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Diamond Proposal Number(s):
[38366]
Open Access
Abstract: Spectro-microscopy is an experimental technique which can be used to observe spatial variations in chemical state and changes in chemical state over time or under experimental conditions. As a result it has broad applications across areas such as energy materials, catalysis, environmental science and biological samples. However, the technique is often limited by factors such as long acquisition times and radiation damage. We present two measurement strategies that allow for significantly shorter experiment times and total doses applied. The strategies are based on taking only a small subset of all the measurements (e.g. sparse acquisition or subsampling), and then computationally reconstructing all unobserved measurements using mathematical techniques. The methods are data-driven, using spectral and spatial importance subsampling distributions to identify important measurements. As a result, taking as little as 4–6% of the measurements is sufficient to capture the same information as in a conventional scan.
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Sep 2026
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Terence
Tan
,
Oliver J.
Clark
,
Matthew J.
Derry
,
Renaud
Duyme
,
Guilherme Abreu
Faria
,
Robert
Farla
,
Ralf
Flaig
,
Yogal Prasad
Ghimirey
,
Anushka
Ghosh
,
Miguel A.
Gomez-Gonzalez
,
Ellen L.
Heeley
,
Anna
Herlihy
,
Annette
Kleppe
,
Paul
Millar
,
Melanie
Nentwich
,
Josh
Pickston
,
Nick
Terrill
,
Armin
Wagner
,
Andrew C.
Walters
,
Matthew
Watson
,
Philippe
Rocca-Serra
,
Susanna-Assunta
Sansone
,
Stephen P.
Collins
Open Access
Abstract: Experiment proposals at synchrotron facilities serve as the primary gateway for instrument access. They currently lack the standardized and granular topic metadata necessary for tasks such as classification and review, and, broadly speaking, reuse. This paper defines and tests the feasibility of a real-time topic classification service for experiment proposals using an open-source machine-learning model and domain experts for the evaluation phase. We applied the OpenAlex topic classification model to 5384 experiment proposals and selected 209 of them to each be independently evaluated by three domain experts to assess the performance and utility of the model. Analysis of the evaluations reveals a general consensus among the reviewers regarding the model's predictions, with a Krippendorff's alpha of 0.572. We also find that 74.2% of the proposals had at least one topic that was unanimously deemed relevant, which suggests that the model performs well enough to be used in a live setting with real-time verification. However, we do not recommend using it in automated environments without human oversight, given the proposal-based precision score of 56.0%. By aligning the data infrastructure of photon and neutron facilities with the OpenAlex ecosystem, we also lay the groundwork for the eventual inclusion of proposals and experiment reports into OpenAlex, which is necessary for a complete record of a research activity.
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Sep 2026
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I23-Long wavelength MX
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Open Access
Abstract: Preventing collisions during automated sample exchange is critical for synchrotron beamlines, particularly for complex cryogenic in-vacuum endstations where recovery from hardware damage may take days. GoniOwl, a compact convolutional neural network (CNN) model, classifies sample-pin presence on the goniometer from a live camera feed on the long-wavelength macromolecular crystallography beamline I23 at Diamond Light Source. Trained on over 8700 manually verified images spanning two years of routine operation and augmented for robustness to illumination changes, camera shifts and occlusions, the model achieves >99% accuracy with millisecond-level inference. A confidence-gating mechanism routes uncertain predictions to a fail-safe path requiring operator confirmation, ensuring suitability for machine-protection control. Integrated via Experimental Physics and Industrial Control System (EPICS) process variables, GoniOwl runs in real time within the automated sample-change sequence. In shadow-mode deployment, the CNN matched or exceeded both the legacy histogram method and operator confirmations, which each achieved 96% accuracy. A closed-loop disagreement-audit workflow automatically collects divergent cases for targeted retraining and verification. The approach is readily transferable to other beamline environments where camera-based vision systems can provide an additional software machine protection layer.
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Sep 2026
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I12-JEEP: Joint Engineering, Environmental and Processing
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Ronan
Docherty
,
Sam
Riley
,
John D.
Morley
,
Evangelos
Papoutsellis
,
Antonia
Bobitan
,
Jack
Donoghue
,
Kathryn
Rankin
,
Fernando
Alvarez-Borges
,
Luis E.
Salinas Farran
,
Stefan
Michalik
,
Alexander
Liptak
,
Genoveva
Burca
,
Pierre-Olivier
Autran
,
Jonathan
Wright
,
Winfred
Kockelmann
,
Olof
Gutowski
,
Ann-Christin
Dippel
,
Martin
Von Zimmermann
,
Dorota
Matras
,
Bartlomiej
Winiarski
,
John S.
Mangum
,
Melissa
Popeil
,
Donal P.
Finegan
,
James A.
Gott
,
Daniela
Proprentner
,
Geoff
West
,
Louis F. J.
Piper
,
Hongyang
Dong
,
Matthew P.
Jones
,
Francesco
Iacoviello
,
Alice V.
Llewellyn
,
Rhodri
Jervis
,
Yuta
Kimura
,
Koji
Amezawa
,
Oki
Sekizawa
,
Mahmoud
Ardakani
,
Aigerim
Omirkhan
,
Mary P.
Ryan
,
James O.
Douglas
,
Siyang
Wang
,
Finn
Giuliani
,
Neil
Mulcahy
,
Shelly
Conroy
,
Chandramohan
George
,
Andrew M.
Beale
,
Simon
Jacques
,
Samuel J.
Cooper
,
Antonios
Vamvakeros
Diamond Proposal Number(s):
[36699, 38628]
Open Access
Abstract: Battery research increasingly relies on advanced imaging, yet open access to such data remains rare, scattered across various sources, and difficult to find. The Battery Imaging Library (BIL) is the first open, curated collection of multi-modal and multi-length scale battery imaging datasets, accompanied by a searchable, FAIR-compliant website. Distinctive features include the release of raw experimental data (radiographs, sinograms, X-ray and electron diffraction patterns) together with rare operando and multi-resolution datasets. Each dataset is linked to Zenodo DOIs with metadata, ensuring persistence and citability; open-source Python scripts for preprocessing and reconstruction are also provided for various CT modalities. BIL enables algorithm benchmarking, machine learning, and teaching using experimental and industrially relevant data. By combining coverage across modalities, length scales, and chemistries with raw data accessibility and a FAIR-aligned web platform, the Battery Imaging Library provides a foundation for openness and reproducibility in battery imaging. The library is available here: https://www.batteryimaginglibrary.com
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Sep 2026
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M.
Ghidini
,
Sergio
Valencia
,
A.
Lesaine
,
R.
Mansell
,
V.
Farenkov
,
X.
Moya
,
N. A.
Stelmashenko
,
F.
Maccherozzi
,
C. W. H.
Barnes
,
F.
Kronast
,
S. S.
Dhesi
,
N. D.
Mathur
Open Access
Abstract: Voltage control of magnetic states in multiferroic heterostructures represents a promising route toward voltage-programmable magnetic devices. However, it remains challenging to achieve deterministic and non-volatile electrical control of well-defined magnetic states without the assistance of a magnetic field. Here we demonstrate repeatable and non-volatile voltage-driven interconversion between giant single-domain and multidomain magnetic states in a strain-coupled heterostructure comprising permalloy (Py) and 0.68Pb(Mg1/3Nb2/3)O3–0.32PbTiO3 (011)pc (PMN-PT) (pc = pseudocubic). Growth-induced in-plane uniaxial magnetic anisotropy yields a square hysteresis loop, indicating a remanent monodomain state that is consistent with 15 µm-diameter XMCD-PEEM images. Low-voltage biasing of the PMN-PT substrate repeatably induces a ∼90° in-plane rotation of the magnetic easy axis and the concomitant formation of a multidomain state. At higher voltages, the easy axis rotates back to its original orientation, restoring the monodomain state. Cycling the ferroelectric substrate through minor loops near the coercive field yields non-volatile and repeatable switching between the multidomain and monodomain states. These results provide a pathway for voltage-programmable engineering of magnetic single-domain states in multiferroic heterostructures.
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Aug 2026
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I11-High Resolution Powder Diffraction
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Diamond Proposal Number(s):
[13284]
Open Access
Abstract: Crystal structure prediction (CSP) from powder diffraction data is a central challenge in materials chemistry. Machine learning (ML) models show promise, but most are trained on idealized simulated data, limiting reliability on real experiments. Here, we assess real-world behaviour using the previously published deCIFer model as an example of PXRD-conditioned generative CSP. deCIFer is an autoregressive transformer that conditions each step of structure generation on encoded PXRD data, guiding token-wise predictions of space group, lattice parameters, and atomic positions. Using controlled robustness tests, we quantify performance under realistic artefacts (noise, background, peak asymmetry, and Scherrer broadening) and introduce metrics for accuracy and predictive uncertainty. deCIFer adapts smoothly to signal distortions and improves over unconditioned baselines when diffraction features remain informative, while expressing appropriate uncertainty as the PXRD pattern becomes underdetermined. Experimental PXRD tests recover the known structures of Si and CeO2 and expose the expected limitations for lower-symmetry Fe2O3 and nanocrystalline CeO2. Overall, ML-based CSP is fundamentally limited by the information content of PXRD, but can accelerate expert workflows by rapidly generating chemically plausible candidates and quantifying uncertainty, making such models valuable human-in-the-loop tools for real-world structure determination.
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Aug 2026
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I04-Macromolecular Crystallography
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Kathryn A.
Giblin
,
Kun
Song
,
Hongming
Chen
,
Weijie
Chen
,
Zhiqiang
Dong
,
Randolph A.
Escobar
,
Tyler P.
Grebe
,
Neil P.
Grimster
,
Alexander W.
Hird
,
Samantha J.
Hughes
,
Jason G.
Kettle
,
Chengzhi
Li
,
Hao
Ma
,
Alexander
Pflug
,
Magdalena
Richter
,
Marianne
Schimpl
,
Haoran
Tang
,
Peng
Wang
,
Gail
Wrigley
,
Ye
Wu
,
Haichang
Yu
,
Robert E.
Ziegler
,
Jason D.
Shields
Diamond Proposal Number(s):
[20015]
Abstract: Generative artificial intelligence (AI) is now widely applied in medicinal chemistry, with detailed case studies emerging in the literature. Here, we describe an early application of REINVENT, AstraZeneca’s in-house generative molecular design platform, to identify new inhibitor scaffolds for hematopoietic progenitor kinase 1 (HPK1). REINVENT was deployed at two stages of the project to address distinct design objectives. For hit identification, transfer learning on kinase-active compounds, followed by reinforcement learning guided by QSAR-based scoring, led to the discovery of three active chemotypes. Subsequently, REINVENT was applied to scaffold hopping, using 3D pharmacophore and docking models as scoring functions, which enabled the identification of two additional active chemotypes. Optimization of one of these scaffolds delivered a compound with potent cellular activity, kinase selectivity, and favorable rat pharmacokinetics. These results demonstrate the value of integrating generative AI with medicinal chemistry expertise and support broader application of the approach in future discovery programs.
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Jul 2026
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I03-Macromolecular Crystallography
I04-Macromolecular Crystallography
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Emily J.
Radley
,
Alessia C.
Andrews
,
Indrek
Kalvet
,
Yunling
Deng
,
Elizabeth L.
Bell
,
Colin W.
Levy
,
Mary
Ortmayer
,
Derren J.
Heyes
,
Clare F.
Megarity
,
Reyes
Núñez-Franco
,
Amy E.
Hutton
,
Yi
Lu
,
David
Baker
,
Anthony P.
Green
Diamond Proposal Number(s):
[38021, 31850]
Abstract: Modern protein design methods based on deep learning allow generation of customized protein scaffolds with diverse geometries and functionalities. Here we capitalize on these recent advances to develop hyper-thermostable de novo CO2 reductases featuring a cobalt porphyrin IX (CoPPIX) cofactor. CoPPIX-containing enzymes were assembled in vivo through media supplementation with cobalt salts and assessed for photocatalytic CO2 reductase activity. We identified two cysteine-ligated designs that exhibit high activity (>1000 turnovers at rates of up to 25 min–1) while suppressing competing hydrogen evolution pathways. A 2.1 Å crystal structure shows close agreement to the design model with the Co–Cys bond programmed as intended. This study showcases the power of computational protein design in developing artificial enzymes to activate challenging molecules such as CO2.
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Jul 2026
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