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Multi-head physics-informed neural networks for learning functional priors and uncertainty quantification
In numerous applications, the integration of prior knowledge and historical information is essential, particularly for tasks requiring the solution of ordinary or partial differential equations (ODEs/PDEs) in data-sparse or noisy environments. For instance, achieving accurate solutions to time-dependent PDEs with limited initial condition measurements necessitates an effective strategy for embedding prior knowledge. Hard-parameter sharing architectures in neural networks (NNs) have demonstrated success in both traditional and scientific machine learning domains, facilitating the learning of informative representations. Here, in this study, we introduce a novel, yet efficient, method to enhance physics-informed neural networks (PINNs) by incorporating a multi-head structure that enables the learning of functional priors from both empirical data and governing physical laws. This prior information can then be used to address data sparsity and high-level noise in solving ODE/PDE problems with uncertainty quantification (UQ). The approach, termed Multi-Head PINN (MH-PINN), consists of a shared body NN and multiple head NNs, each corresponding to an individual PINN instance. Our framework for functional prior learning is carried out in two stages: (1) training the MH-PINNs to develop a shared body NN alongside multiple head NNs, and (2) employing these trained head NNs to estimate a prior distribution through a normalizing flow-based density estimator. The learned functional prior can then be applied as a regularization mechanism in deterministic contexts or as an informative prior within a Bayesian inference framework, aiding in the resolution of subsequent ODE/PDE tasks. We evaluate the efficacy of MH-PINNs across five benchmark problems, including a high-dimensional parametric PDE, all characterized by data sparsity or substantial noise levels. Our findings reveal that MH-PINNs deliver accurate solutions and robust UQ, demonstrating adaptability across a range of complex and challenging scenarios.
Theoretical and experimental study of highly textured GaAs on silicon using a graphene buffer layer
A novel heteroepitaxial growth technique, quasi-van der Waals epitaxy, promises the ability to deposit three-dimensional GaAs materials on silicon using two-dimensional graphene as a buffer layer by overcoming the lattice and thermal expansion mismatch. In this study, density functional theory (DFT) simulations were performed to understand the interactions at the GaAs/graphene hetero-interface as well as the growth orientations of GaAs on graphene. To develop a better understanding of the molecular beam epitaxy-grown GaAs films on graphene, samples were characterized by x-ray diffraction (..theta..-2..theta.. scan, ..omega..-scan, grazing incidence XRD and pole figure measurement) and transmission electron microscopy. The realizations of smooth GaAs films with a strong (111) oriented fiber-texture on graphene/silicon using this deposition technique are a milestone towards an eventual demonstration of the epitaxial growth of GaAs on silicon, which is necessary for integrated photonics application.
Analysis of transient fission gas behaviour in oxide fuel using BISON and TRANSURANUS
The modelling of fission gas behaviour is a crucial aspect of nuclear fuel analysis in view of the related effects on the thermo-mechanical performance of the fuel rod, which can be particularly significant during transients. Experimental observations indicate that substantial fission gas release (FGR) can occur on a small time scale during transients (burst release). To accurately reproduce the rapid kinetics of burst release in fuel performance calculations, a model that accounts for non-diffusional mechanisms such as fuel micro-cracking is needed. In this work, we present and assess a model for transient fission gas behaviour in oxide fuel, which is applied as an extension of diffusion-based models to allow for the burst release effect. The concept and governing equations of the model are presented, and the effect of the newly introduced parameters is evaluated through an analytic sensitivity analysis. Then, the model is assessed for application to integral fuel rod analysis. The approach that we take for model assessment involves implementation in two structurally different fuel performance codes, namely, BISON (multi-dimensional finite element code) and TRANSURANUS (1.5D semi-analytic code). The model is validated against 19 Light Water Reactor fuel rod irradiation experiments from the OECD/NEA IFPE (International Fuel Performance Experiments) database, all of which are simulated with both codes. The results point out an improvement in both the qualitative representation of the FGR kinetics and the quantitative predictions of integral fuel rod FGR, relative to the canonical, purely diffusion-based models, with both codes. The overall quantitative improvement of the FGR predictions in the two codes is comparable. Furthermore, calculated radial profiles of xenon concentration are investigated and compared to experimental data, demonstrating the representation of the underlying mechanisms of burst release by the new model.
Fabrication and characterization of metal–semiconductor–metal ultraviolet photodetector based on rutile TiO{sub 2} nanorod
The fabrication and characterization of a metal–semiconductor–metal ultraviolet photodetector are studied. The photodetector is based on TiO{sub 2} nanorods (NRs) grown on p-type (1 1 1)-oriented silicon substrate seeded with a TiO{sub 2} layer is synthesized by radio frequency reactive magnetron sputtering. A chemical bath deposition is used to grow TiO{sub 2} NRs on Si substrate. The structural and optical properties of the obtained sample are analyzed by using X-ray diffraction and field emission-scanning electron microscopy. Results show a tetragonal rutile structure of the synthesized TiO{sub 2} NRs. Optical properties are further examined using photoluminescence spectroscopy. A sharp and high-intensity UV peak at 367 nm is observed in comparison with visible defect peaks centered at 432 and 718 nm. Upon exposure to 365 nm light (2.3 mW/cm) at 5 V bias, the device displays 76.06 × 10{sup 2} sensitivity, internal photodetector gain of 77.06, photocurrent of the device is 2.62 × 10{sup −5} A and photoresponse peak of 69.7 mA/W. The response and recovery times are calculated as 18.5 and 19.1 ms upon illumination to a pulse UV light (365 nm, 2.3 mW/cm{sup 2}) at 5 V applied bias. These results demonstrate that the fabricated high-quality photodiode is a promising candidate as a low-cost UV photodetector for commercially-integrated photoelectronic applications.
Radiation testing campaign results for understanding the suitability of FPGAs in detector electronics
SRAM based Field Programmable Gate Arrays (FPGAs) have been rarely used in High Energy Physics (HEP) due to their sensitivity to radiation. The last generation of commercial FPGAs based on 28 nm feature size and on Silicon On Insulator (SOI) technologies are more tolerant to radiation to the level that their use in front-end electronics is now feasible. FPGAs provide re-programmability, high-speed computation and fast data transmission through the embedded serial transceivers. They could replace custom application specific integrated circuits in front end electronics in locations with moderate radiation field. Finally, the use of a FPGA in HEP experiments is only limited by our ability to mitigate single event effects induced by the high energy hadrons present in the radiation field.
Characterisation and testing of CHEC-M—A camera prototype for the small-sized telescopes of the Cherenkov telescope array
The Compact High Energy Camera (CHEC) is a camera design for the Small-Sized Telescopes (SSTs; 4 m diameter mirror) of the Cherenkov Telescope Array (CTA). The SSTs are focused on very-high-energy γ-ray detection via atmospheric Cherenkov light detection over a very large area. This implies many individual units and hence cost-effective implementation, as well as shower detection at large impact distance, and hence large field of view (FoV), and efficient image capture in the presence of large time gradients in the shower image detected by the camera. CHEC relies on dual-mirror optics to reduce the plate-scale and make use of 6 x 6 mm 2 pixels, leading to a low-cost (~150 k€), compact (0.5 m x 0.5 m), and light (~45 kg) camera with 2048 pixels providing a camera FoV of ~9 degrees. The CHEC electronics are based on custom TARGET (TeV array readout with GSa/s sampling and event trigger) application-specific integrated circuits (ASICs) and field programmable gate arrays (FPGAs) sampling incoming signals at a gigasample per second, with flexible camera-level triggering within a single backplane FPGA. CHEC is designed to observe in the γ-ray energy range of 1–300 TeV, and at impact distances up to 500 m. To accommodate this and provide full flexibility for later data analysis, full waveforms with 96 samples for all 2048 pixels can be read out at rates up to ~900 Hz. The first prototype, CHEC-M, based on multi-anode photomultipliers (MAPMs) as photosensors, was commissioned and characterised in the laboratory and during two measurement campaigns on a telescope structure at the Paris Observatory in Meudon. Here in this paper, the results and conclusions from the laboratory and on-site testing of CHEC-M are presented. They have provided essential input on the system design and on operational and data analysis procedures for a camera of this type. In conclusion, a second full-camera prototype based on Silicon photomultipliers (SiPMs), addressing the drawbacks of CHEC-M identified during the first prototype phase, has already been built and is currently being commissioned and tested in the laboratory.
The Timepix4 analog front-end design: Lessons learnt on fundamental limits to noise and time resolution in highly segmented hybrid pixel detectors
This manuscript describes the optimization of the front-end readout electronics for high granularity hybrid pixel detectors. The theoretical study aims at minimizing the noise and jitter. The model presented here is validated with both circuit post layout simulations and measurements on the Timepix4 Application Specific Integrated Circuit (ASIC). The analog front-end circuit and the procedure to optimize the dimensions of the main transistors are described with detail. The Timepix4 is the most recent ASIC designed in the framework of the Medipix4 Collaboration. It was manufactured in 65nm CMOS process, and consists of a four side buttable matrix of 448 × 512 pixels with 55µm pitch. The analog front-end has a gain of ~36mV/ke - when configured in High Gain Mode, and ~20mV/ke - when configured in Low Gain Mode. The Equivalent Noise Charge (ENC) is ~68e - rms and ~80e - rms in High Gain Mode and in Low Gain Mode respectively. In event driven mode the incoming hits can be time stamped within a ~ 200ps time bin and the chip can deal with a maximum flux of ~ 3.6MHzmm –2 s –1 . In photon counting mode, the chip can deal with up to ~ 5GHzmm –2 s –1 . The routine designed to optimize the Timepix4 front-end is then used to analyze the performance limits in terms of jitter and noise for Charge Sensitive Amplifiers in pixel detectors.
nmRanalysis: An Open-Source Web Application for Semi-automated NMR Metabolite Profiling
Though data acquisition and initial signal pre-processing of nuclear magnetic resonance (NMR) spectra have achieved high degrees of automation, downstream processing - specifically the profiling of spectra - has bottlenecked the overall NMR analysis workflow. Several efforts have been made to mitigate this bottleneck, but these solutions often trade an increase in automation for limitations elsewhere. Here, in this technical note, we introduce nmRanalysis, a user-friendly web-application that integrates the strengths of existing profiling tools for a more automated profiling workflow. nmRa-nalysis additionally incorporates novel features, including a machine-learning-driven recommender system for me-tabolite identification, further increasing the utility of nmRanalysis over the individual tools that it incorporates.
Accelerators for Classical Molecular Dynamics Simulations of Biomolecules
Atomistic Molecular Dynamics (MD) simulations provide researchers the ability to model biomolecular structures such as proteins and their interactions with drug-like small molecules with greater spatiotemporal resolution than is otherwise possible using experimental methods. MD simulations are notoriously expensive computational endeavors that have traditionally required massive investment in specialized hardware to access biologically relevant spatiotemporal scales. Our goal is to summarize the fundamental algorithms that are employed in the literature to then highlight the challenges that have affected accelerator implementations in practice. We consider three broad categories of accelerators: Graphics Processing Units (GPUs), Field-Programmable Gate Arrays (FPGAs), and Application Specific Integrated Circuits (ASICs). These categories are comparatively studied to facilitate discussion of their relative trade-offs and to gain context for the current state of the art. We conclude by providing insights into the potential of emerging hardware platforms and algorithms for MD.
Investigation of Oxidation Mechanisms in HfS 2 and ZrS 2 via In Situ Electron Microscopy
Transition metal dichalcogenides (TMD) combine semiconducting properties with stability and are therefore widely studied for future microelectronic devices. However, applications require integration with a suitable dielectric at a planar, defect-free interface. For Hf- and Zr-based TMDs, the good dielectric properties of hafnia and zirconia imply that direct oxidation is a promising strategy, provided that a high-quality interface and suitable oxide morphology can be formed. Here, in this study, we investigate HfS 2 and ZrS 2 oxidation, aiming to understand pathways toward planar oxide layer formation and the mechanisms that determine the characteristics of the semiconductor/dielectric interface. We use conventional and environmental transmission electron microscopy to reveal changes in morphology and composition of the TMDs under different gaseous environments. We show that oxidation at ambient conditions causes compositional heterogeneities, with local replacement of S by O in both materials. Thermal oxidation causes desulfurization and formation of a smooth but defective oxide layer. In contrast, plasma oxidation appears the best-suited to forming a controlled TMD/dielectric interface with a smooth oxide layer and without major defects. These results provide insight into the opportunities available from HfS 2 and ZrS 2 through oxidation and suggest a materials processing strategy for electronic device fabrication.
Black Phosphorus for Mid-Infrared Optoelectronics: Photophysics, Scalable Processing, and Device Applications
High efficiency mid-infrared (λ = 3-8 μm) light emitters and photodetectors are pivotal for advancing next-generation optoelectronics. However, narrow-bandgap semiconductors face fundamental challenges such as pronounced nonradiative carrier recombination and thermally generated noise, which impede device performance. Recently, two-dimensional layered black phosphorus (BP) and its alloys have attracted substantial interest for mid-infrared device applications, demonstrating superior performance relative to conventional III-V and II-VI semiconductors with similar bandgaps. In this review, we discuss the optical properties of BP, contrasting these with those of covalent compounds. Owing to its inherently self-terminated surface structure and reduced nonradiative recombination, BP exhibits high performance in light emission and photodetection at room temperature. Furthermore, this review highlights recent advances in the large-area processing of BP thin films, paving the way for practical device applications and integration. Finally, we explore ongoing challenges and emerging opportunities in the utilization of BP for functional mid-infrared devices.
X-ray Micro-Computed Tomography for Structural Analysis of All-Solid-State Battery at Pouch Cell Level
Characterizing the microstructure of all-solid-state batteries (ASSBs) during fabrication and operation is vital for their advancement, particularly as scaling to pouch cell levels introduces challenges in probing large-scale microstructural evolution. This work highlights the potential of synchrotron X-ray micro-computed tomography (sXCT) as a nondestructive, rapid (<30 min), and high-resolution technique for visualizing and quantifying key microstructural features, including overhang, porosity, contact loss, active surface area, and tortuosity, in all-solid-state pouch cells. The large field of view (up to millimeters) of sXCT enables detailed analysis at an industry-relevant scale, bridging the gap between laboratory research and commercial applications. Furthermore, integrating realistic sXCT-derived 3D models into multiphysics simulations could provide insights into chemo-mechanical degradation, particularly at the edges of the pouch cells, offering a pathway for designing robust, high-performance ASSBs. This perspective establishes sXCT as an indispensable tool for advancing both the understanding and the engineering of next-generation energy storage systems.
Stimulated Emission from Below the Bandgap in Giant Quantum Shells
Colloidal semiconductor nanocrystals (NCs) have emerged as promising candidates for developing solution-processable optical gain media, with potential applications in integrated photonic circuits and lasers. However, the deployment of NCs in these technologies has been hindered by nonradiative Auger recombination of multiexciton states, which shortens the optical gain lifetime and reduces its spectral range. Here, we demonstrate that these limitations can be overcome by using giant colloidal quantum shells (g-QSs), comprising a quantum-confined CdSe shell grown over a large (~14 nm) CdS bulk core. Such bulk-nanoscale architecture minimizes exciton–exciton interactions, leading to suppressed Auger recombination and one of the broadest gain bandwidths reported for colloidal nanomaterials, spanning energies both above and, remarkably, below the bandgap. Ultrafast transient absorption and photoluminescence measurements demonstrate that the high-energy portion of optical gain arises from states containing more than 15 excitons per particle while the unusual sub-bandgap gain behaviour results from an Auger-assisted radiative recombination, a mechanism that has traditionally been viewed as a loss pathway. Altogether, these results reveal a unique gain regime associated with bulk-nanocrystal hybrid systems, which offers a promising path toward solution-processable light sources.
Valley photonic crystals for control of spin and topology
Photonic crystals offer unprecedented opportunity for light manipulation and applications in optical communication and sensing1,2,3,4. Exploration of topology in photonic crystals and metamaterials with non-zero gauge field has inspired a number of intriguing optical phenomena such as one-way transport and Weyl points5,6,7,8,9,10. Recently, a new degree of freedom, valley, has been demonstrated in two-dimensional materials11,12,13,14,15. Here, we propose a concept of valley photonic crystals with electromagnetic duality symmetry but broken inversion symmetry. We observe photonic valley Hall effect originating from valley-dependent spin-split bulk bands, even in topologically trivial photonic crystals. Valley–spin locking behaviour results in selective net spin flow inside bulk valley photonic crystals. We also show the independent control of valley and topology in a single system that has been long pursued in electronic systems, resulting in topologically-protected flat edge states. Valley photonic crystals not only offer a route towards the observation of non-trivial states, but also open the way for device applications in integrated photonics and information processing using spin-dependent transportation.
Giant electric field-induced second harmonic generation in polar skyrmions
Electric field-induced second harmonic generation allows electrically controlling nonlinear light-matter interactions crucial for emerging integrated photonics applications. Despite its wide presence in materials, the figures-of-merit of electric field-induced second harmonic generation are yet to be elevated to enable novel device functionalities. Here, we show that the polar skyrmions, a topological phase spontaneously formed in PbTiO3/SrTiO3 ferroelectric superlattices, exhibit a high comprehensive electric field-induced second harmonic generation performance. The second-order nonlinear susceptibility and modulation depth, measured under non-resonant 800 nm excitation, reach ~54.2 pm V −1 and ~664% V −1 , respectively, and high response bandwidth (higher than 10 MHz), wide operating temperature range (up to ~400 K) and good fatigue resistance (>10 10 cycles) are also demonstrated. Through combined in-situ experiments and phase-field simulations, we establish the microscopic links between the exotic polarization configuration and field-induced transition paths of the skyrmions and their electric field-induced second harmonic generation response. Our study not only presents a highly competitive thin-film material ready for constructing on-chip devices, but opens up new avenues of utilizing topological polar structures in the fields of photonics and optoelectronics.
High-resolution national mapping of natural gas composition substantially updates methane leakage impacts
Methane is emitted from oil and gas operations alongside heavier hydrocarbons and non-hydrocarbon gases, shaping emissions management decision-making, including air quality impacts. Yet, most assessments assume fixed gas composition, overlooking significant spatial and temporal variations. Here, we generate a high-resolution, data-driven map of natural gas composition across the United States, reconstructing methane, heavier hydrocarbons, and non-hydrocarbon species using spatio-temporal interpolation and oil-and-gas production patterns. Our approach is able to reduce composition prediction errors by 39% in terms of Mean Absolute Error (MAE) compared to standard techniques and reveals that methane loss rates have been underestimated by more than 50% in some regions. Beyond methane, we uncover substantial variability in co-emitted gases, exposing blind spots in current emissions inventories and emissions management frameworks. Our work enables more accurate emissions assessments, guides targeted measurement strategies, and informs emissions management decision-making. It also provides a general framework for prediction in environmental applications that integrate sparse measurements with auxiliary variables.
Application of Deep Learning on Integrating Prediction, Provenance, and Optimization
In this research, we investigated two approaches to detect job anoma- lies and/or contention for large scale computing efforts: 1. Preemptive job scheduling using binomial classification long short-term memory networks 2. Forecasting intra-node computing loads from the active jobs and addi- tional job(s) For approach 1, we achieved a 14% improvement in computational resources utilization and an overall classification accuracy of 85% on real tasks executed in a High Energy Physics computing workflow. For this paper, we present the preliminary results used in second approach.