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At least 505 records · Page 28

Optical neural engine for solving scientific partial differential equations

Abstract Solving partial differential equations (PDEs) is the cornerstone of scientific research and development. Data-driven machine learning (ML) approaches are emerging to accelerate time-consuming and computation-intensive numerical simulations of PDEs. Although optical systems offer high-throughput and energy-efficient ML hardware, their demonstration for solving PDEs is limited. Here, we present an optical neural engine (ONE) architecture combining diffractive optical neural networks for Fourier space processing and optical crossbar structures for real space processing to solve time-dependent and time-independent PDEs in diverse disciplines, including Darcy flow equation, the magnetostatic Poisson’s equation in demagnetization, the Navier-Stokes equation in incompressible fluid, Maxwell’s equations in nanophotonic metasurfaces, and coupled PDEs in a multiphysics system. We numerically and experimentally demonstrate the capability of the ONE architecture, which not only leverages the advantages of high-performance dual-space processing for outperforming traditional PDE solvers and being comparable with state-of-the-art ML models but also can be implemented using optical computing hardware with unique features of low-energy and highly parallel constant-time processing irrespective of model scales and real-time reconfigurability for tackling multiple tasks with the same architecture. The demonstrated architecture offers a versatile and powerful platform for large-scale scientific and engineering computations.

Tang, Yingheng (ORCID:0009000153622546)↗

One-shot learning for solution operators of partial differential equations

Learning and solving governing equations of a physical system, represented by partial differential equations (PDEs), from data is a central challenge in many areas of science and engineering. Traditional numerical methods can be computationally expensive for complex systems and require complete governing equations. Existing data-driven machine learning methods require large datasets to learn a surrogate solution operator, which could be impractical. Here, we propose a solution operator learning method that requires only one PDE solution, i.e., one-shot learning, along with suitable initial and boundary conditions. Leveraging the locality of derivatives, we define a local solution operator in small local domains, train it using a neural network, and use it to predict solutions of new input functions via mesh-based fixed-point iteration or meshfree neural-network based approaches. We test our method on various PDEs, complex geometries, and a practical spatial infection spread application, demonstrating its effectiveness and generalization capabilities.

97 MATHEMATICS AND COMPUTING↗

In situ formed partially disordered phases as earth-abundant Mn-rich cathode materials

Abstract Earth-abundant cathode materials are urgently needed to enable scaling of the Li-ion industry to multiply terawatt hours of annual production, necessitating reconsideration of how good cathode materials can be obtained. Irreversible transition metal migration and phase transformations in Li-ion cathodes are typically believed to be detrimental because they may trigger voltage hysteresis, poor kinetics and capacity degradation. Here we challenge this conventional consensus by reporting an unusual phase transformation from disordered Li- and Mn-rich rock salts to a new phase (named δ ), which displays partial spinel-like ordering with short coherence length and exhibits high energy density and rate capability. Unlike other Mn-based cathodes, the δ phase exhibits almost no voltage fade upon cycling. We identify the driving force and kinetics of this in situ cathode formation and establish design guidelines for Li- and Mn-rich compositions that combine high energy density, high rate capability and good cyclability, thereby enabling Mn-based energy storage.

25 ENERGY STORAGE↗

Structure of Pb(Fe 2/3 W 1/3 )O 3 single crystals with partial cation order

Despite intensive studies on the complex perovskite Pb(Fe 2/3 W 1/3 )O 3 (PFWO) relaxor, understanding the exact nature of its multifunctional properties has remained a challenge for decades. In this work we report a comprehensive structural study of the PFWO single crystals using a combination of synchrotron X-ray diffraction and high-resolution electron microscopy. The set of {h + ½, k + ½, l + ½} superlattice reflections was observed for the first time based on single-crystal synchrotron X-ray experiments (100–450 K) and transmission electron microscopy investigations, which indicates some kind of B-cation ordering in PFWO which had been thought to be totally disordered. It was found that (1) the crystal structure of PFWO should be described by a partly ordered cubic perovskite (i.e. Fm – 3m ), (2) the weak ferromagnetic properties and excess magnetic moment of PFWO can be understood based on non-random distribution of Fe cations between the 4a and 4b sites, and (3) the Pb displacement disorder is present in this material and the cations are probably displaced along the <100> directions. The X-ray diffraction results of this investigation show that partial cation ordering indeed exists in PFWO, which makes it necessary to revisit the generally accepted interpretations of the results obtained up to date. In agreement with X-ray diffraction study the main results of TEM study include: (1) a long range order that can be described with the Fm – 3m symmetry is reliably detected, (2) the coherence length of that long range order is in the order of 1–2 nm and (3) no remarkable chemical inhomogeneity is found in the tested PFWO crystal, excluding the possibility of a compositional ordering arising from substitutional defects in the perovskite structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Machine-learning-based spectral methods for partial differential equations

Spectral methods are an important part of scientific computing’s arsenal for solving partial differential equations (PDEs). However, their applicability and effectiveness depend crucially on the choice of basis functions used to expand the solution of a PDE. The last decade has seen the emergence of deep learning as a strong contender in providing efficient representations of complex functions. In the current work, we present an approach for combining deep neural networks with spectral methods to solve PDEs. In particular, we use a deep learning technique known as the Deep Operator Network (DeepONet) to identify candidate functions on which to expand the solution of PDEs. We have devised an approach that uses the candidate functions provided by the DeepONet as a starting point to construct a set of functions that have the following properties: (1) they constitute a basis, (2) they are orthonormal, and (3) they are hierarchical, i.e., akin to Fourier series or orthogonal polynomials. We have exploited the favorable properties of our custom-made basis functions to both study their approximation capability and use them to expand the solution of linear and nonlinear time-dependent PDEs. The proposed approach advances the state of the art and versatility of spectral methods and, more generally, promotes the synergy between traditional scientific computing and machine learning.

97 MATHEMATICS AND COMPUTING↗

A localized view on molecular dissociation via electron-ion partial covariance

Inner-shell photoelectron spectroscopy provides an element-specific probe of molecular structure, as core-electron binding energies are sensitive to the chemical environment. Short-wavelength femtosecond light sources, such as Free-Electron Lasers (FELs), even enable time-resolved site-specific investigations of molecular photochemistry. Here, we study the ultraviolet photodissociation of the prototypical chiral molecule 1-iodo-2-methylbutane, probed by extreme-ultraviolet (XUV) pulses from the Free-electron LASer in Hamburg (FLASH) through the ultrafast evolution of the iodine 4d binding energy. Methodologically, we employ electron-ion partial covariance imaging as a technique to isolate otherwise elusive features in a two-dimensional photoelectron spectrum arising from different photofragmentation pathways. The experimental and theoretical results for the time-resolved electron spectra of the 4d 3/2 and 4d 5/2 atomic and molecular levels that are disentangled by this method provide a key step towards studying structural and chemical changes from a specific spectator site.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Multi-resolution partial differential equations preserved learning framework for spatiotemporal dynamics

Traditional data-driven deep learning models often struggle with high training costs, error accumulation, and poor generalizability in complex physical processes. Physics-informed deep learning (PiDL) addresses these challenges by incorporating physical principles into the model. Most PiDL approaches regularize training by embedding governing equations into the loss function, yet this depends heavily on extensive hyperparameter tuning to weigh each loss term. To this end, we propose to leverage physics prior knowledge by “baking” the discretized governing equations into the neural network architecture via the connection between the partial differential equations (PDE) operators and network structures, resulting in a PDE-preserved neural network (PPNN). This method, embedding discretized PDEs through convolutional residual networks in a multi-resolution setting, largely improves the generalizability and long-term prediction accuracy, outperforming conventional black-box models. The effectiveness and merit of the proposed methods have been demonstrated across various spatiotemporal dynamical systems governed by spatiotemporal PDEs, including reaction-diffusion, Burgers’, and Navier-Stokes equations.

97 MATHEMATICS AND COMPUTING↗

Ozone pollution reduction partially offsets the negative impact of climate change mitigation efforts on global hunger

Studies warning of the potential negative effects of climate mitigation on food security through the competing use of land for bioenergy and afforestation have overlooked the impact of reduced ozone and its potential enhancement of crop yields. Here we use six global agro-economic models to compare the impacts of climate change with climate mitigation policy and ozone reduction on agriculture. We find that ozone reduction could reduce the negative impact of a 1.5 °C-consistent climate change mitigation policy on global hunger by 15% in 2050. Sub-Saharan Africa and India, where hunger is most severe, account for 56% of this global reduction. Our findings indicate that the negative effects of climate mitigation on global hunger could be partially offset by the ozone reduction impact.

54 ENVIRONMENTAL SCIENCES↗

Magneto-structural studies of an unusual [Mn III Mn II Gd III (OR) 4 ] 4– partial cubane from 2,2'-bis- p- t Bu-calix[4]arene

Reaction of 2,2'-bis- p - t Bu-calix[4]arene (H 8 L) with MnCl 2 ·4H 2 O, GdCl 3 ·6H 2 O and 2,6-pyridinedimethanol (H 2 pdm) affords [Mn III Mn II Gd III (H 3 L)(pdmH)(pdm)(MeOH) 2 (dmf)]·3MeCN·dmf ( 3 ·3MeCN·dmf) upon vapour diffusion of MeCN into the basic dmf/MeOH mother liquor. 3 crystallises in the tetragonal space group P 4 1 2 1 2 with the asymmetric unit comprising the entire cluster. The highly unusual core contains a triangular arrangement of Mn III Mn II Gd III ions housed within a [Mn III Mn II Gd III (OR) 4 ] 4– partial cubane. Magnetic susceptibility and magnetisation data reveal best fit parameters J Mn(II)–Mn(III) = +0.415 cm –1 , J Mn(III)–Gd(III) = +0.221 cm –1 , J Mn(II)–Gd(III) = –0.258 cm –1 and D Mn(III) = –4.139 cm –1 . Theoretically derived magnetic exchange interactions, anisotropy parameters, and magneto-structural correlations for 3 are in excellent agreement with the experimental data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Partially reduced Ru/RuO 2 composites as efficient and pH-universal electrocatalysts for hydrogen evolution

Efficient and long-term stable electrocatalysts for the hydrogen evolution reaction (HER) via water splitting are urgently desired to ease the energy crisis and develop the sustainability of human society. However, the HER performance of state-of-the-art Pt in non-acidic solutions is unsatisfactory due to the severely sluggish kinetics. In this work, DFT theoretical calculations reveal that the Ru/RuO 2 composites enable high HER activity to be pursued under non-acidic conditions because of the distinctive Ru and RuO 2 interface, which possess not only a strong capability to adsorb and dissociate water but also appropriate binding energies of H and OH. Therefore, we employ a simple strategy, including heating under an oxygen-poor environment and/or in situ electrochemical reduction, to partially reduce RuO 2 . The formed Ru/RuO 2 interfaces demonstrate superior HER activities (e.g. η 10 = 17 mV, 35 mV dec –1 in 1 M KOH) than Pt/C (e.g. η 10 = 27 mV, 58 mV dec –1 in 1 M KOH) at both small (10–100 mA cm –2 ) and large (1 A cm –2 ) current densities in alkaline solution and even real seawater. Comprehensive experiments were conducted to investigate the structure-HER performance relationships. Moreover, benefiting from the bifunctional character of RuO2, a two-electrode system based on Ru/RuO 2 composites and RuO 2 exhibits the lowest cell voltage for water splitting in both 1 M KOH and 0.5 M H 2 SO 4 , respectively. A 300 h-stability test at 10 mA cm –2 without an obvious decay demonstrates the industrial prospects of the Ru/RuO 2 composites to generate green energy.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Integrated in situ spectroscopic studies on syngas production from partial oxidation of methane catalyzed by atomically dispersed rhodium cations on ceria

Catalytic reforming of methane to produce syngas is an important strategy for producing value-added chemicals. The conventional reforming catalyst relies on supported nickel nanoparticles. Here, in this work, we investigated singly dispersed Rh cations anchored on a CeO 2 catalyst (Rh 1 /CeO 2 ) for high activity and selectivity towards the production of syngas via partial oxidation of methane (POM) in the temperature range of 600–700 °C. The yields of H 2 and CO at 700 °C are 83% and 91%, respectively. The anchored Rh 1 atoms on CeO 2 of Rh 1 /CeO 2 are in the cationic state, and on an average each Rh 1 atom coordinates with 4–5 surface lattice oxygen atoms of CeO 2 . Compared to inert CeO 2 for POM, via the incorporation of single-atom sites, Rh 1 modifies the electronic state of oxygen atoms proximal to the Rh 1 atoms and thus triggers the catalytic activity of CeO 2 . The high activity of single-atom catalyst Rh 1 /CeO 2 suggests that the incorporation of single atoms of transition metals to the surface of a reducible oxide can modulate the electronic state of proximal anions of the oxide support toward forming an electronic state favorable for the selective formation of ideal products.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Methane partial oxidation under periodic reaction conditions on Pt/Al 2 O 3

The increasing interest in utilizing methane, the primary component of natural gas, for chemical production has spurred research into methane partial oxidation (MPO) as an alternative to traditional steam methane reforming (SMR). MPO has lower energy requirements and potential for carbon capture, making it an attractive option for hydrogen production. Challenges remain, however, such as carbon deposition leading to degradation and achieving high hydrogen selectivity. Here, the impact of periodic reactor operation on MPO over a Pt/Al 2 O 3 catalyst was studied, primarily via varying reactor inlet compositions. Experiments were conducted using periodic operation strategies to assess the influence of changing reactant inlet concentrations on hydrogen formation during MPO. The results suggest that cycling between mixtures with low and high oxygen content can lead to transient hydrogen formation rates that surpass those achieved at steady state. Control experiments and density functional theory (DFT) calculations show that enhanced hydrogen formation can be attributed to the reaction between CO with hydroxyl groups at the metal and alumina support interface. Furthermore, this work underscores the critical role of surface coverages at the metal–support interface and suggests avenues for future exploration, including alternative support materials with higher OH mobility and changes in the cycling scheme to enhance catalyst performance under periodic conditions.

03 NATURAL GAS↗

Emergence of heavy-fermion behavior and distorted square nets in partially vacancy-ordered Y 4 Fe x Ge 8 (1.0 ≤ x ≤ 1.5)

Disorders in intermetallic systems belonging to the CeNiSi 2 -family are frequently overlooked. Even compounds presumed to be stoichiometric, such as YFeGe 2 , can be misidentified. Here, in this study, we report a series of Y 4 Fe x Ge 8 (1.0 ≤ x ≤ 1.5) compounds and show, using high-resolution synchrotron X-ray diffraction, that they feature asymmetrical structural distortions in the Fe and Ge sites that lead to a superstructure with partially ordered Fe vacancies and distorted Ge square-net in the triclinic crystal system, space group P[1 with combining macron] with a = 11.4441(3) Å, b = 32.7356(7) Å, c = 11.4456(3) Å, α = 79.6330(10)°, β = 88.3300(10)°, and γ = 79.6350 (10)°. The unit cell is 16 times the conventional orthorhombic cell with the space group Cmcm. We identified the lower and upper limits for Fe in Y 4 Fe x Ge 8 (1.0 ≤ x ≤ 1.5). Our physical property measurements yielded a Sommerfeld coefficient γ = 39.8 mJ mole −1 K −2 , a Kadowaki–Woods ratio of 1.2 × 10 −5 μΩ cm mole 2 K 2 mJ −2 , and a Wilson ratio of 1.83, suggesting heavy fermion behavior in the absence of f electrons, a rather rare case. Furthermore, we observed strong spin frustration and noted findings indicating possible superconductivity associated with the Fe content.

Zhao, Hengdi [Argonne National Laboratory (ANL), A↗

Probabilistic error estimation for non-intrusive reduced models learned from data of systems governed by linear parabolic partial differential equations

This work derives a residual-based a posteriori error estimator for reduced models learned with non-intrusive model reduction from data of high-dimensional systems governed by linear parabolic partial differential equations with control inputs. It is shown that quantities that are necessary for the error estimator can be either obtained exactly as the solutions of least-squares problems in a non-intrusive way from data such as initial conditions, control inputs, and high-dimensional solution trajectories or bounded in a probabilistic sense. Here, the computational procedure follows an offline/online decomposition. In the offline (training) phase, the high-dimensional system is judiciously solved in a black-box fashion to generate data and to set up the error estimator. In the online phase, the estimator is used to bound the error of the reduced-model predictions for new initial conditions and new control inputs without recourse to the high-dimensional system. Numerical results demonstrate the workflow of the proposed approach from data to reduced models to certified predictions.

97 MATHEMATICS AND COMPUTING↗

Wisconsin In Situ Penning (WISP) gauge: A versatile neutral pressure gauge to measure partial pressures in strong magnetic fields

A new type of in-vessel Penning gauge, the Wisconsin In Situ Penning (WISP) gauge, has been developed and successfully operated in the Wendelstein 7-X (W7-X) island divertor baffle and vacuum vessel. The capacity of the quantitative measurements of the neutral reservoir for light impurities, in particular, helium, is important for tokamaks as well as stellarator divertors in order to avoid fuel dilution and radiative energy loss. Penning gauges assisted by spectroscopy are a powerful tool to obtain the total neutral pressure as well as fractional neutral pressures of specific impurities. The WISP gauge is a miniaturized Penning gauge arrangement, which exploits the ambient magnetic field of magnetic confinement fusion experiments to establish the Penning discharge. Then, in situ spectroscopy is conducted to separate the fractional neutral pressures of hydrogen, helium, and possibly also other impurities. The WISP probe head was qualified using the magnetic field of the Magnetized Dusty Plasma Experiment at Auburn University between 0.25 T and 3.5 T [E. Thomas et al ., J. Plasma Phys. 81, 345810206 (2015)]. The in-depth quantitative evaluation for hydrogen and helium will be shown as well as an exploration of nitrogen, argon, and neon. A power law scaling between current I and pressure p, I = f (Gas,V) p n⁽Gas, B⁾ , was shown. The factor f is gas and anode potential dependent, while n is gas and magnetic field strength dependent. Pressure measurements from 0.1 mbar and down to 1 × 10 -5 mbar were achieved, demonstrating a reliable operating range for relevant pressure levels in the divertor and main vessel regions in current and future fusion devices, with a time resolution of up to 1 kHz. The lowest achievable pressure measurement increases with an increase in B and can be shifted with the anode potential V . At W7-X, the WISP probe head was mounted on an immersion tube setup that passes through the cryostat and places the probe head close to the plasma. Two probe heads were positioned in different divertor pump gaps, top and bottom, and one close to the plasma on the midplane in one module. The gauges were in situ calibrated together with the ASDEX pressure gauges [G. Haas and H.-S. Bosch, Vacuum 51, 39 (1998)]. Data were taken during the entire operation phase 1.2b, and measurements were coherent with other neutral gas pressure gauges. For the spectroscopic partial pressure measurements, channels of a spectroscopic detection system based on photo-multipliers, a so-called filterscope [R. J. Colchin et al ., Rev. Sci. Instrum. 74, 2068 (2003)], provided by the Oak Ridge National Lab were used.

47 OTHER INSTRUMENTATION↗

Ultrashort XUV pulse absorption spectroscopy of partially oxidized cobalt nanoparticles

High-order harmonic generation (HHG) based transient extreme ultraviolet (XUV) absorption spectroscopy is an emerging technique to trace photoinduced charge carrier dynamics in condensed phase materials with femtosecond and even attosecond temporal resolution and elemental specificity. However, its application to nanoparticulate samples that are relevant, for example, for novel photocatalytic light harvesting concepts, has been limited. This is in part due to the challenge to produce residual-free samples on ultrathin, XUV-transparent substrates as well as a widespread understanding that sparsely distributed nanoparticles do not provide sufficient contrast for XUV absorption measurements. Here, we present static XUV absorption spectra of partially oxidized Co nanowire-structures with diameters of approximately 4.5 nm and lengths between 10 and 40 nm, recorded with an ultrashort pulse HHG light source. Nanoparticles are synthesized by the agglomeration of Co atoms inside superfluid helium droplets, followed by surface deposition and oxidation in ambient air. The method is uniquely suited for residual-free synthesis of transition metal nanowires and their deposition on ultrathin substrates. Analysis by high-resolution transmission electron microscopy reveals the formation of CoO nanowires with regions of unoxidized Co in their interior. The nanoparticle samples are investigated in an HHG-driven ultrafast XUV absorption setup. Despite the low surface coverage of only 23%, the recorded spectrum exhibits a distinct absorption feature at the Co M 2,3 (2p) edge near 60 eV with a peak height of about 40 mOD. Finally, the results support the feasibility of table-top ultrafast transient XUV absorption studies of photoinduced dynamics in transition metal oxide nanoparticles with sub-monolayer surface coverage.

74 ATOMIC AND MOLECULAR PHYSICS↗