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DOE OSTI2022

SMALE: Enhancing Scalability of Machine Learning Algorithms on Extreme-Scale Computing Platforms

Deployment and execution of machine learning tasks on extreme-scale computing platforms face several significant technical challenges: 1) High computing cost incurred by dense networks – The computing workload of deep networks with densely-connected topology increases rapidly with the network size, imposing a non-scalable computing model of extreme-scale computing platforms; 2) Non-optimized workload distribution – Many advanced deep learning algorithms, e.g., sparsification and irregular net-work topology, produce very unbalanced workload distribution on extreme-scale computing platforms. The computation efficiency is greatly hindered by the incurred data and computation redundancies as well as long tails of the node with extensive workload; 3) Constraints in data movement and I/O bottle-neck – Inter-node data movement in extreme-scale computing platforms are associated with high energy and latency costs, and subject to the constraints of I/O bandwidth; and 4) Generalization of algorithm realization and acceleration on computing platforms – The large varieties of machine learning algorithms and structures of extreme-scale computing platforms make the derivation of a generalized algorithm realization and acceleration method very challenging, which, however, is the requirement by domain scientists and interested users. We call the above challenges Smale’s Problems in Machine Learning and Understanding for High-Performance Computing Scientific Discovery. The objective of our three-year research project is to develop a holistic innovation set at structure, assembly, and acceleration layers of machine learning algorithms to address the above challenges in algorithm deployment and execution. Three tasks are particularly performed, including: At the algorithm structure level, we investigate the techniques that can structurally sparsify on the topology of deep networks for computing workload reduction. We also study clustering and pruning techniques that can optimize the workload distributions over the extreme-scale computing platforms; At the algorithm assembly level, we derive a unified learning framework for unsupervised transfer learning and dynamic growing capabilities. Novel training methods are also exploited to enhance the training efficiency of the proposed framework; At the algorithm acceleration level, we will develop a series of techniques that can accelerate the computation of sparse matrix operations, which are one of the core executions in deep learning and optimize memory access of the concerned platforms. Our proposed techniques attack the fundamental problems in machine learning algorithms running on extreme-scale computing platforms by vertically integrating the solutions at three closely entangled layers, paving the long-term scaling path of machine learning applications under DOE context. Three tasks corresponding to the above respective research orientations are performed during the three-year project period with our collaborators at ORNL. The outcome of the proposed project is anticipated to form a holistic solution set of novel algorithms and network topologies, efficient training techniques, and fast acceleration methods to promote the computing scalability of the machine learning applications of particular interest to DOE.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2022

Onion-Like nanoparticles of the metal–organic framework UiO-66 synthesized by sequential spike crystal growth

Reproducible size and morphology of metal–organic framework (MOF) particles are essential for tuning these materials for applications like heterogeneous catalysis. Particle size measurements from electron micrographs provide information about the impact of changing synthetic parameters on MOF growth, but reported data are usually limited to averages and statistical distributions. Further elucidating synthetic control parameters could facilitate studies of facet-dependent and non-classical growth mechanisms. Here, in this work, phase-pure nanoparticles of the MOF UiO-66 were synthesized using alternating spikes of zirconium and hafnium-based nodes, forming an onion-like structure with alternating layers of Zr-UiO-66 and Hf-UiO-66. In conventional, bright-field transmission electron micrographs, Hf-UiO-66 and Zr-UiO-66 layers appeared darker and lighter, respectively. Alternating layers of Hf-UiO-66 and Zr-UiO-66 were also apparent in elemental maps obtained using scanning transmission electron microscopy with energy-dispersive X-ray spectroscopy. Average particle sizes increased monotonically with each spike, and no Zr-only or Hf-only particles were observed after the first spike, indicating that new material was consistently incorporated onto existing seeds. Surface area of the onion-like particles and their uptake of Ni 2+ from solution were both between the values measured for Zr-UiO-66 and Hf-UiO-66. Results demonstrate the use of sequential spikes of nodes in monitoring crystal growth in MOFs.

36 MATERIALS SCIENCE↗

DOE OSTI2026

Direct observation of diamond formation in a shock-compressed high explosive

Understanding the formation timescale and structure of carbonaceous reaction products is critical for modeling the high-pressure equation-of-state of organic materials. We use the National Ignition Facility to shock-compress polycrystalline TATB (C 6 H 6 N 6 O 6 ) samples to ~70–130 GPa and ~4000–5500 K, employing in situ nanosecond X-ray diffraction to probe reaction products and velocimetry to measure transmitted compression wave profiles. Our diffraction data is consistent with the formation of diamond over timescales less than ~60 ns. This represents carbon condensation from a molecular explosive on timescales three times faster than previously reported and the earliest observation of diamond produced from reacting TATB. Reactive flow simulations with explicit chemistry reproduce the observed temporal structure within wave profiles to inform the distribution of P-T states. These findings provide direct evidence of ultrafast diamond formation in a reactive system at extreme conditions and provide new constraints for models of shock and detonation chemistry.

Clarke, Samantha M. [Lawrence Livermore National L↗

DOE OSTI2015

3D distribution of interstellar medium in the Galaxy: Preparation for analysis of Gaia observations

Accurate and detailed three-dimensional (3D) maps of Galactic interstellar medium (ISM) are still lacking. One way to obtain such 3D descriptions is to record a large set of individual absorption or reddening measurements toward target stars located at various known distances and directions. The inversion of these measurements using a tomographic method can produce spatial distribution of the ISM. Until recently absorption data were very limited and distances to the target stars are still uncertain, but the situation will greatly improve thanks to current and future massive stellar surveys from ground, and to Gaia mission. To prepare absorption data for inversion from a huge number of stellar spectra, automated tools are needed. We have developed various spectral analysis tools adapted to different type of spectra, early- or late- type star. We also have used diffuse interstellar bands (DIBs) to trace IS structures and kinematics. Although we do not know yet their carriers, they can be a promising tool to trace distant interstellar clouds or Galactic arms. We present some examples of the interstellar fitting and show the potentiality of DIBs in tracing the ISM. We will also briefly show and comment the latest 3D map of the local ISM which reveal nearby cloud complexes and cavities.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Turbulent flow characteristics in an 84-pin rod bundle for typical and damaged spacer grids

Hexagonal rod bundles arranged in a tightly packed triangular lattice are extensively used for heat transfer and energy generation applications. Staggered spacer grids are used to maintain the structural integrity of gas-cooled fast reactor (GFR) fuel assemblies, while inducing localized turbulence in flow. Damage to these spacer grids results in a disruption of flow fields within these hexagonal fuel bundles. Experimental flow visualizations are critical to identify the differences in local flow properties that the structural damage may cause. This experimental research investigates the flow-field characteristics at a near-wall and center plane in a prototypical 84-pin GFR fuel assembly. Newly installed typical spacers and spacers subject to naturally occurring damage due to material degradation over prolonged experimentation were investigated. Velocity fields were acquired by utilizing the matched-index-of-refraction method to obtain time-resolved particle image velocimetry measurements for a Reynolds number of 12 000. Reynolds decomposition statistical results divulged differences in the time-averaged velocity, velocity fluctuations, flow anisotropy, and Reynolds stress distributions. Galilean decomposition demarcated the influence of spacer grid damage on the velocity fields. To extract turbulent structures and elucidate mechanisms of flow instabilities, proper orthogonal decomposition analysis was employed. Reduced order flow reconstructions enabled the application of vortex identification algorithms to determine the spatial and statistical characteristics of vortices generated. This research work provides unique experimental data on the spacer grid condition-dependent flow. The results offer a deeper understanding of fluid dynamics behavior to support GFR rod bundle design efforts and computational fluid dynamics model validation.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Deep-learning-based canopy height model generation from sub-meter resolution panchromatic satellite imagery

Canopy height models (CHMs) with sufficient resolution to distinguish individual trees are useful for a variety of applications. However, standard techniques to acquire such data, such as airborne lidar surveying, are often prohibitively expensive. Deep learning techniques for generating CHMs from high-resolution imagery are an attractive option to reduce costs. To date, success with these methods has been demonstrated using multichannel aerial photography and specialized satellite data products derived from multiple sensors, neither of which is commonly available at temporal resolutions finer than one year. Here we demonstrate a method to generate sub-meter resolution CHMs in three forests in California using a more abundant data source: sub-meter resolution, panchromatic satellite imagery from a single sensor. We show that phenology and species composition play important roles in model transferability; when trained using imagery from a single conifer forest in autumn, the model performs well on autumn imagery from a second conifer forest several hundred kilometers distant with no re-training. With modest additions to the training dataset, the same model generates minimally biased estimates of canopy height in both conifer and deciduous forests during multiple seasons. Because the model operates on satellite data with global coverage and a relatively short return interval, we propose its suitability to extrapolate tree-level canopy height data to remote regions and conduct high-temporal resolution monitoring of forest structure. We furthermore demonstrate the workflow’s applicability to fire modeling by conducting simulations in forests populated by trees measured using both this approach and airborne lidar surveying. We find minimal differences in fire behavior relative to a baseline case in which only statistical distributions of tree height and crown area are known. This result underscores the value of forest structural information derived from our workflow for improving the fidelity of wildland fire simulations, among other ecological applications.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2018

PDFgetN3: Atomic Pair Distribution Functions From Neutron Powder Diffraction Data Using ad hoc Corrections

We describe PDFgetN3, a new software tool for the extraction of PDF from neutron powder diffraction intensity data. We demonstrate its use with constant wavelength neutron data measured at the new powder diffractometer PEARL at the Delft University of Technology (TU), Delft. PDFgetN3 uses an ad hoc data collection protocol similar to PDFgetX3. Here, the quality of the resulting PDFs is assessed by structure refinement and by comparison with established results from synchrotron x-ray scattering.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

DP-TwoLevel: two-stage gradient subspace learning for differentially private federated learning

Federated learning (FL) enables collaborative model training across distributed data sources without sharing raw data, but faces fundamental challenges in communication efficiency and privacy. Differentially private (DP) training mitigates information leakage but introduces noise that degrades model performance, especially in high-dimensional settings. We propose DP-TwoLevel, a hierarchical gradient projection method that improves utility under fixed DP constraints by exploiting low-dimensional structure in model updates. Our approach learns a two-level PCA-based representation of gradients and applies DP noise in a reduced-dimensional subspace, thereby lowering the effective noise magnitude while preserving dominant signal components. We evaluate the method across three datasets (MNIST, Fashion-MNIST, CIFAR-10) and three privacy regimes (ϵ∈0.5, 1.0, 2.0). Across nine experimental settings, DP-TwoLevel consistently outperforms DP-FedAvg, achieving an average accuracy improvement of 9.44%, with larger gains observed in lower ϵ(higher-noise) regimes (up to +22.31%). We further analyze scalability across models ranging from 100K to 1.49M parameters and identify a variance-based success criterion: performance remains strong when the projection preserves more than 75% of gradient variance, degrades in a marginal regime (65–75%), and fails below this threshold. Our results demonstrate that structure-aware dimensionality reduction can significantly improve the privacy–utility tradeoff in FL without modifying formal privacy guarantees. We also provide empirical evidence of scaling limitations for global projections and motivate per-layer extensions for larger models.

Kotevska, Olivera [ORNL] (ORCID:0000000316772243)↗

DOE OSTI2015

Testing the Delayed Gamma Capability in MCNP6

The mission of the Domestic Nuclear Detection Office is to quickly and reliably detect unauthorized attempts to import or transport special nuclear material for use against the United States. Developing detection equipment to meet this objective requires accurate simulation of both the detectable signature and detection mechanism. A delayed particle capability was initially added to MCNPX 2.6.A in 2005 to sample the radioactive fission product parents and emit decay particles resulting from the decay chain. To meet the objectives of detection scenario modeling, the capability was designed to sample a particular time for emitting particular multiplicity of a particular energy. Because the sampling process of selecting both time and energy is interdependent, to linearize the time and emission sampling, atom densities are computed at several discrete time steps, and the time-integrated production is computed by multiplying the atom density by the decay constant and time step size to produce a cumulative distribution function for sampling the emission time, energy, and multiplicity. The delayed particle capability was initially given a time-bin structure to help reasonably reproduce, from a qualitative sense, a fission benchmark by Beddingfield, which examined the delayed gamma emission. This original benchmark was only qualitative and did not contain the magnitudes of the actual measured data but did contain relative graphical representation of the spectra. A better benchmark with measured data was later provided by Hunt, Mozin, Reedy, Selpel, and Tobin at the Idaho Accelerator Center; however, because of the complexity of the benchmark setup, sizable systematic errors were expected in the modeling, and initial results compared to MCNPX 2.7.0 showed errors outside of statistical fluctuation. Presented in this paper is a more simplified approach to benchmarking, utilizing closed form analytic solutions to the granddaughter equations for particular sets of decay systems. We examine five different decay chains (two-stage decay to stable) and show the predictability of the MCNP6 delayed gamma feature. Results do show that while the default delayed gamma calculations available in the MCNP6 1.0 release can give accurate results for some isotopes (e.g., 137 Ba), the percent differences between the closed form analytic solutions and the MCNP6 calculations were often >40% ( 28 Mg, 28 Al, 42 K, 47 Ca, 47 Sc, 60 Co). With the MCNP6 1.1 Beta release, the tenth entry on the DBCN card allows improved calculation within <5% as compared to the closed form analytic solutions for immediate parent emissions and transient equilibrium systems. While the tenth entry on the DBCN card for MCNP6 1.1 gives much better results for transient equilibrium systems and parent emissions in general, it does little to improve daughter emissions of secular equilibrium systems. Finally, hypotheses were presented as to why daughter emissions of secular equilibrium systems might be mispredicted in some cases and not in others.

42 ENGINEERING↗

DOE OSTI2017

An Atomistic Carbide-Derived Carbon Model Generated Using ReaxFF-Based Quenched Molecular Dynamics

We report a novel atomistic model of carbide-derived carbons (CDCs), which are nanoporous carbons with high specific surface areas, synthesis-dependent degrees of graphitization, and well-ordered, tunable porosities. These properties make CDCs viable substrates in several energy-relevant applications, such as gas storage media, electrochemical capacitors, and catalytic supports. These materials are heterogenous, non-ideal structures and include several important parameters that govern their performance. Therefore, a realistic model of the CDC structure is needed in order to study these systems and their nanoscale and macroscale properties with molecular simulation. We report the use of the ReaxFF reactive force field in a quenched molecular dynamics routine to generate atomistic CDC models. The pair distribution function, pore size distribution, and adsorptive properties of this model are reported and corroborated with experimental data. Simulations demonstrate that compressing the system after quenching changes the pore size distribution to better match the experimental target. Ring size distributions of this model demonstrate the prevalence of non-hexagonal carbon rings in CDCs. Furthermore, these effects may contrast the properties of CDCs against those of activated carbons with similar pore size distributions and explain higher energy densities of CDC-based supercapacitors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2016

Faraday rotation measure synthesis of intermediate redshift quasars as a probe of intervening matter

There is evidence that magnetized material along the line of sight to distant quasars is detectable in the polarization properties of the background sources. The polarization properties appear to be correlated with the presence of intervening Mg ii absorption, which is thought to arise in outflowing material from star forming galaxies. In order to investigate this further, we have obtained high spectral resolution polarization measurements, with the Very Large Array and the Australia Telescope Compact Array, of a set of 49 unresolved quasars for which we have high quality optical spectra. These enable us to produce a Faraday Depth spectrum for each source, using Rotation Measure (RM) Synthesis. Our new independent radio data confirms that interveners are strongly associated with depolarization. We characterize the complexity of the Faraday Depth spectrum using a number of parameters and show how these are related, or not, to the depolarization and to the presence of Mg ii absorption along the line of sight. We argue that complexity and structure in the Faraday Depth distribution likely arise from both intervening material and intrinsically to the background source and attempt to separate these. We find that the strong radio depolarization effects associated with intervening material at redshifts out to z≈1 arise from inhomogeneous Faraday screens producing a dispersion in RM across individual sources of around 10 rad m{sup −2}. This is likely produced by disordered fields with strengths of at least 3 μG.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2016

MICROWAVE IMAGING OF A HOT FLUX ROPE STRUCTURE DURING THE PRE-IMPULSIVE STAGE OF AN ERUPTIVE M7.7 SOLAR FLARE

Corona structures and processes during the pre-impulsive stage of solar eruption are crucial to understanding the physics leading to the subsequent explosive energy release. Here we present the first microwave imaging study of a hot flux rope structure during the pre-impulsive stage of an eruptive M7.7 solar flare, with the Nobeyama Radioheliograph at 17 GHz. The flux rope is also observed by the SDO/AIA in its hot passbands of 94 and 131 Å. In the microwave data, it is revealed as an overall arcade-like structure consisting of several intensity enhancements bridged by generally weak emissions, with brightness temperatures (T{sub B}) varying from ∼10,000 K to ∼20,000 K. Locations of microwave intensity enhancements along the structure remain relatively fixed at certain specific parts of the flux rope, indicating that the distribution of emitting electrons is affected by the large-scale magnetic configuration of the twisted flux rope. Wavelet analysis shows a pronounced 2 minute period of the microwave T{sub B} variation during the pre-impulsive stage of interest. The period agrees well with that reported for AIA sunward-contracting loops and upward ejective plasmoids (suggested to be reconnection outflows). This suggests that both periodicities are controlled by the same reconnection process that takes place intermittently at a 2 minute timescale. We infer that at least a part of the emission is excited by non-thermal energetic electrons via the gyro-synchrotron mechanism. The study demonstrates the potential of microwave imaging in exploring the flux rope magnetic geometry and relevant reconnection process during the onset of solar eruption.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2021

Improving the Accessibility and Usability of Geothermal Information with Data Lakes and Data Pipelines on the Geothermal Data Repository: Preprint

The Geothermal Data Repository (GDR) provides universal access to data and information resulting from research and development activities funded by the Department of Energy (DOE). The GDR has extended this universal access to big data through integration with data lakes developed by the Open Energy Data Initiative (OEDI). Previously, large datasets such as seismic waveform or distributed acoustic sensing (DAS) data could only be accessed by institutions with high performance data storage and compute capabilities, effectively limiting the accessibility of big data to national labs, larger universities, and major corporations. Moreover, the time and resources needed to transport big data and configure them can produce additional barriers to use. Many of the standard formats used for structured data models (also known as content models) are incapable of handling big data and can introduce additional usability problems, often requiring data to be reformatted prior to use. This paper will explore how recent integrations between the GDR and the OEDI data lake have improved the accessibility and usability of geothermal data in a big way, making the data available to a broader audience, and enabling collaborative analysis and innovation across the greater geothermal industry.

access↗

DOE OSTI2021

Improving the Accessibility and Usability of Geothermal Information with Data Lakes and Data Pipelines on the Geothermal Data Repository

access↗

DOE OSTI2023

Exploring the gravitational structure of the proton with the dilepton final state using the CLAS12 detector at Jefferson Lab

The Gravitational Form Factors (GFFs) give access to the internal distributions of mass, pressure and shear force inside the proton. They were considered experimentally unmeasurable for decades due to the very weak gravitational interaction [1]. However, the Generalized Parton Distributions (GPDs), which describe the correlations between the longitudinal momentum and the transverse position of the partons inside the nucleon, have lately been related to the GFFs. For the first time, this relation gives the opportunity to extract GFFs experimentally. In this talk, I will present two ways to access GFFs using data taken in 2018 by the CLAS12 detector with a 10.6 GeV electron beam impinging on a liquid-hydrogen target. First, I will present the first measurement of the Timelike Compton Scattering reaction (the hard photoproduction of a lepton pair), that gives access to the quark GFFs via the angular asymmetry of the electron/positron pair [2]. I will then present the current effort to extract the near-threshold J/? photoproduction cross section using the same dataset. This later measurement is expected to provide direct insight on the gluons GFFs of the proton. [1] H. PAGELS. Energy-Momentum Structure Form Factors of Particles. Phys.Rev. 144 (1966) 1250-1260 [2] P. Chatagnon. First Measurement of Timelike Compton Scattering. Phys.Rev.Lett. 127 (2021) 26, 262501

Chatagnon, Pierre↗

DOE OSTI2023

Finding simplicity: unsupervised discovery of features, patterns, and order parameters via shift-invariant variational autoencoders *

Abstract Recent advances in scanning tunneling and transmission electron microscopies (STM and STEM) have allowed routine generation of large volumes of imaging data containing information on the structure and functionality of materials. The experimental data sets contain signatures of long-range phenomena such as physical order parameter fields, polarization, and strain gradients in STEM, or standing electronic waves and carrier-mediated exchange interactions in STM, all superimposed onto scanning system distortions and gradual changes of contrast due to drift and/or mis-tilt effects. Correspondingly, while the human eye can readily identify certain patterns in the images such as lattice periodicities, repeating structural elements, or microstructures, their automatic extraction and classification are highly non-trivial and universal pathways to accomplish such analyses are absent. We pose that the most distinctive elements of the patterns observed in STM and (S)TEM images are similarity and (almost-) periodicity, behaviors stemming directly from the parsimony of elementary atomic structures, superimposed on the gradual changes reflective of order parameter distributions. However, the discovery of these elements via global Fourier methods is non-trivial due to variability and lack of ideal discrete translation symmetry. To address this problem, we explore the shift-invariant variational autoencoders (shift-VAEs) that allow disentangling characteristic repeating features in the images, their variations, and shifts that inevitably occur when randomly sampling the image space. Shift-VAEs balance the uncertainty in the position of the object of interest with the uncertainty in shape reconstruction. This approach is illustrated for model 1D data, and further extended to synthetic and experimental STM and STEM 2D data. We further introduce an approach for training shift-VAEs that allows finding the latent variables that comport to known physical behavior. In this specific case, the condition is that the latent variable maps should be smooth on the length scale of the atomic lattice (as expected for physical order parameters), but other conditions can be imposed. The opportunities and limitations of the shift VAE analysis for pattern discovery are elucidated.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

A Comparison of Phasor Communication Protocols

In this paper, we compare three approaches used for continuous transfer of real-time synchrophasor data: IEEE C37.118.2-2011, IEC TR 61850-90-5 and a new protocol being developed under the Department of Energy (DOE) project DOE-OE-859 called the Streaming Telemetry Transport Protocol (STTP). STTP is currently being advanced as a potential third standard protocol via the IEEE Power Engineering Society STTP P10 Work Group (P2664). Each of these three synchrophasor protocols is described in detail in this paper along with the basis for their operating characteristics using Internet Protocol (IP) transport. The dominant protocol for the exchange of synchrophasor data is IEEE C37.118, both in the U.S. and internationally. The most recent IEEE C37.118 standard is broken into two parts where IEEE C37.118.1-2011 (Part 1) defines the normative synchrophasor measurement requirements and IEEE C37.118.2-2011 (Part 2) defines the protocol’s data transmission format. Both the IEC TR 61850-90-5 and the emergent STTP specifications only address synchrophasor data transmission; therefore, this paper focuses on comparing the data transmission protocol elements of these standards. The primary dimensions of comparison of these three protocols explored in this paper are: structure, efficiency, susceptibility to data loss, scalability, security, and other operability functionality. Additionally, though not specifically relevant to synchrophasor data telemetry, the three protocols are evaluated with respect to flexibility for transporting non-synchrophasor precise-time data streams.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2017

Role of Short-Range Chemical Ordering in (GaN) 1– x (ZnO) x for Photodriven Oxygen Evolution

(GaN) 1–x (ZnO) x (GZNO) is capable of visible-light driven water splitting, but its bandgap at x ≤ 0.15 (>2.7 eV) results in poor visible-light absorption. Unfortunately, methods to narrow its bandgap by incorporating higher ZnO concentrations are accompanied by extensive Urbach tailing near the absorption-edge, which is indicative of structural disorder or chemical inhomogeneities. We evaluated whether this disorder is intrinsic to the bond-length distribution in GZNO or is a result of defects introduced from the loss of Zn during nitridation. Here, the synthesis of GZNO derived from layered double hydroxide (LDH) precursors is described which minimizes Zn loss and chemical inhomogeneities and enhances visible-light absorption. The average and local atomic structures of LDH-derived GZNO were investigated using X-ray and neutron scattering and are correlated with their oxygen evolution rates. An isotope-contrasted neutron-scattering experiment was conducted in conjunction with reverse Monte Carlo (RMC) simulations. We showed that a bond-valence bias in the RMC refinements reproduces the short-range ordering (SRO) observed in structure refinements using isotope-contrasted neutron data. The findings suggest that positional disorder of cation–anion pairs in GZNO partially arises from SRO and influences local bond relaxations. Furthermore, particle-based oxygen evolution reactions (OERs) in AgNO 3 solution reveal that the crystallite size of GZNO correlates more than positional disorder with oxygen evolution rate. In conclusion, these findings illustrate the importance of examining the local structure of multinary photocatalysts to identify dominant factors in particulate-based photodriven oxygen evolution.

36 MATERIALS SCIENCE↗