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At least 1,045 records · Page 58

Multi-objective Bayesian optimization of ferroelectric materials with interfacial control for memory and energy storage applications

Optimization of materials’ performance for specific applications often requires balancing multiple aspects of materials’ functionality. Even for the cases where a generative physical model of material behavior is known and reliable, this often requires search over multidimensional function space to identify low-dimensional manifold corresponding to the required Pareto front. In this work, we introduce the multi-objective Bayesian optimization (MOBO) workflow for the ferroelectric/antiferroelectric performance optimization for memory and energy storage applications based on the numerical solution of the Ginzburg–Landau equation with electrochemical or semiconducting boundary conditions. MOBO is a low computational cost optimization tool for expensive multi-objective functions, where we update posterior surrogate Gaussian process models from prior evaluations and then select future evaluations from maximizing an acquisition function. Using the parameters for a prototype bulk antiferroelectric (PbZrO 3 ), we first develop a physics-driven decision tree of target functions from the loop structures. We further develop a physics-driven MOBO architecture to explore multidimensional parameter space and build Pareto-frontiers by maximizing two target functions jointly—energy storage and loss. This approach allows for rapid initial materials and device parameter selection for a given application and can be further expanded toward the active experiment setting. The associated notebooks provide both the tutorial on MOBO and allow us to reproduce the reported analyses and apply them to other systems (https://github.com/arpanbiswas52/MOBO_AFI_Supplements).

36 MATERIALS SCIENCE↗

Ghidra Galore

SAND2025-00232O Ghidra Galore is a software tool that provides a test harness for running an analysis script in multiple versions of Ghidra, an open-source binary disassembler, using Docker. It allows users to understand how different versions of Ghidra evolve over time by analyzing a corpus of input files. The software includes a Python script that wraps the Docker functionality, simplifying the process of running the analysis script across different Ghidra versions. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Crussell, Jonathan [Sandia National Lab. (SNL-CA),↗

Suitability of Altair FEKO's Computational Electromagnetic Techniques for Tree Model Scattering and Penetration at Ku-band

Foliage penetration (FOPEN) radar at lower frequencies (VHF, UHF) is a well-studied area with many contributions. However, there is growing interest in using higher Ku-band frequencies (12-18 GHz) for FOPEN. Specifically, the reduced wavelength sizes provide some key saliencies for developing more optimized detection solutions. The disadvantage is that exploiting Ku-band for FOPEN is complicated because higher frequencies have pronounced scattering effects due to their smaller wavelengths. A methodology h as been developed to model and simulate FOPEN problems that characterize the phenomenology of Ku-band electromagnetic ( EM ) wave transmissions through moderate foliage. The details of this research (i.e. the realistic tree models, simulation setup and results) are documented in multiple reports. The main focus of this report is to describe the preliminary validation and verification of Altair FEKO, the computational EM (CEM) software used for this research, as well as present a simplified symmetrical tree model and an introductory CAD tree model.

42 ENGINEERING↗

Dynamic and Responsive Distributed Energy Resource Education Solutions for Building, Fire, and Safety Department Officials (Final Technical Report)

From April 2021 through March 2024, the Interstate Renewable Energy Council (IREC) led a collaborative project to develop a free online clearinghouse of educational resources about solar photovoltaics (PV), energy storage systems (ESS), electric vehicle supply equipment (EVSE), and grid-interactive efficient building (GEB) technologies. Two websites—the Clean Energy Clearinghouse and CleanEnergyTraining.org—housed over 70 educational resources. Over the course of the three-year project, 154,272 unique visitors accessed the learning materials. Learner feedback was overwhelmingly positive. Even through the end of the project, there was sustained demand for education and communication. A primary innovation of the project was to drive multiple complementary audiences to the same place. Building owners, designers, installation contractors and developers, authorities having jurisdiction (AHJs), and fire service personnel all benefit from a shared understanding of clean energy technologies, including safety and code-related requirements. When considering the impact on the target audience, the project team worked with partners and advisors to inform resource creation and delivery in such a way as to address key motivational factors of the target audience and compel each user to seek additional information on the topic and return to the Clean Energy Clearinghouse website as their central location for more information. Resources were intentionally developed to be concise—five to 15 minutes—and accessible, meaning not overly technical. Providing basic information demystified the technologies and invited the professional to explore additional learning opportunities. Awardee and partner collaboration was key to project success. IREC facilitated collaboration among the other Topic 2 awardees, Southface and New Buildings Institute (NBI). The three awardees shared relevant information gained through discovery and validation questionnaires that informed product development and reduced duplication of effort by coordinating the development of complementary, and not competing, educational resources. Inspired by this collaboration, IREC brought on additional partners even in the final year of the project. Five regional energy efficiency organizations were part of the project, which expanded the connection between efficiency and distributed energy resources. We also included resources on the Clearinghouse that were developed through other federally funded projects, such as the Buildings Energy Efficiency Frontiers & Innovation Technologies (BENEFIT) program. The website was developed with the learner in mind, and not solely the funding source. Feedback from stakeholders throughout the project, and especially in its final year, indicated the need for continued education and facilitated communication among stakeholders to further the safe and widespread adoption of clean energy.

14 SOLAR ENERGY↗

Online eco-routing for electric vehicles using combinatorial multi-armed bandit with estimated covariance

Identifying energy-efficient routes in real-time has significant implications for the energy-optimal operations of electric vehicles (EVs). Here, this study proposes a novel model for EV online eco-routing problem, which obtains the minimal expected energy consumption paths (MECPs) for multiple origin-destination (OD) pairs simultaneously. Specifically, we formulate the routing problem as a bandit problem and solve it with online algorithms. We extend the algorithms by implementing a path elimination mechanism to reduce the candidate path set and introducing the variance and covariance of the energy consumption to reduce the uncertainties. The numerical results show that the proposed algorithms can efficiently obtain near-optimal MECPs, and the solution is significantly better than the widely used shortest trip time path algorithm (STTP) and shortest trip distance path algorithm (SDP). The variation considering link energy covariance and path elimination generates paths that save 4.1% of energy compared to the SDP and 5.4% to the STTP.

33 ADVANCED PROPULSION SYSTEMS↗

Fast and stable deep-learning predictions of material properties for solid solution alloys

We present a novel deep learning (DL) approach to produce highly accurate predictions of macroscopic physical properties of solid solution binary alloys and magnetic systems. The major idea is to make use of the correlations between different physical properties in alloy systems to improve the prediction accuracy of neural network (NN) models. We use multitasking NN models to simultaneously predict the total energy, charge density and magnetic moment. These physical properties mutually serve as constraints during the training of the multitasking NN, resulting in more reliable DL models because multiple physics properties are correctly learned by a single model. Two binary alloys, copper–gold (CuAu) and iron–platinum (FePt), were studied. Our results show that once the multitasking NN's are trained, they can estimate the material properties for a specific configuration hundreds of times faster than first-principles density functional theory calculations while retaining comparable accuracy. We used a simple measure based on the root-mean-squared errors to quantify the quality of the NN models, and found that the inclusion of charge density and magnetic moment as physical constraints leads to more stable models that exhibit improved accuracy and reduced uncertainty for the energy predictions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Capturing membrane structure and function in lattice Boltzmann models

Here, we develop a mesoscopic approach to model the nonequilibrium behavior of membranes at the cellular scale. Relying on lattice Boltzmann methods, we develop a solution procedure to recover the Nernst-Planck equations and Gauss's law. A general closure rule is developed to describe mass transport across the membrane, which is able to account for protein-mediated diffusion based on a coarse-grained representation. We demonstrate that our model is able to recover the Goldman equation from first principles and show that hyperpolarization occurs when membrane charging dynamics are controlled by multiple relaxation timescales. The approach provides a promising way to characterize non-equilibrium behaviors that arise due to the role of membranes in mediating transport based on realistic three-dimensional cell geometries.

59 BASIC BIOLOGICAL SCIENCES↗

A python package for analyzing Resilience of Complex Systems (pyRoCS) v.0.0

SAND2024-01040O PyRoCS software synthesizes mathematical equations from several domains—including information theory, ecology, and engineering sciences—to support resilience analysis for complex systems. Resilience is the ability of the complex system being analyzed to withstand, operate through, and recover from a disruption. The complex system can be a physical system such as an electric grid, an organization such as a company, or even a subfunction of an organization. Existing mathematical equations for resilience analysis are found within multiple domains including information theory, biological sciences, and complex systems. This package synthesizes and refactors equations from these various domains to make them more generalizable for application across different types of complex systems relevant for resilience analysis. Users will be able to apply these equations to characterize different components of complex systems based on available data. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Verzi, Stephen↗

BULKI-Store v0.3.2

BULKI-Store is a distributed object storage system optimized for high-performance computing environments. Built with a Rust core and Python bindings, it efficiently manages scientific and machine learning datasets across HPC clusters. The system employs a client-server architecture with MPI integration, enabling seamless scaling on supercomputers like Perlmutter. BULKI-Store's object-oriented approach provides intuitive data organization with rich metadata support, contrasting with traditional file-based solutions. Key optimizations include selective checkpoint loading, unified checkpoint files, and object chunking for large data transfers. For machine learning workloads, BULKI-Store offers advantages through fine-grained access patterns, dynamic data sharing between training instances, and reduced memory pressure. Memory management features include strategic Python GC calls, minimized data copies, and batch processing capabilities. The system leverages Rayon's thread pool for asynchronous data prefetching and supports multiple CPU architectures (ARM64, x86, AMD, RISC-V). By combining performance optimizations with developer-friendly APIs, BULKI-Store addresses the complex data management challenges of modern HPC applications while maintaining compatibility across heterogeneous computing environments.

Zhang, Wei [Lawrence Berkeley National Laboratory ↗

Overcoming the field-of-view to diameter trade-off in microendoscopy via computational optrode-array microscopy

High-resolution microscopy of deep tissue with large field-of-view (FOV) is critical for elucidating organization of cellular structures in plant biology. Microscopy with an implanted probe offers an effective solution. However, there exists a fundamental trade-off between the FOV and probe diameter arising from aberrations inherent in conventional imaging optics (typically, FOV < 30% of diameter). Here, we demonstrate the use of microfabricated non-imaging probes (optrodes) that when combined with a trained machine-learning algorithm is able to achieve FOV of 1x to 5x the probe diameter. Further increase in FOV is achieved by using multiple optrodes in parallel. With a 1 × 2 optrode array, we demonstrate imaging of fluorescent beads (including 30 FPS video), stained plant stem sections and stained living stems. Our demonstration lays the foundation for fast, high-resolution microscopy with large FOV in deep tissue via microfabricated non-imaging probes and advanced machine learning.

59 BASIC BIOLOGICAL SCIENCES↗

Evolving efforts to maintain and improve XPS analysis quality in an era of increasingly diverse uses and users

Based on literature analysis, X-ray photoelectron spectroscopy (XPS) use continues to increase exponentially. This increased use is accompanied by anecdotal reports and systematic analyses indicating a growing presence of significantly flawed data analyses. Recognition of this problem within the surface analysis community has increased with an understanding that both inexperienced users and increased use of XPS outside the surface analysis community contribute to the problem. The XPS community has initiated several efforts to help address the problem, which is not unique to XPS. This paper describes some of the specific problems identified and some of the community efforts intended to address them. Here, we describe activities focused on three specific issues: (i) requests for detailed guides and protocols and bite-sized versions of information for non-experts, (ii) incomplete data and analysis reporting, and (iii) the high rate of peak fitting problems. A 2019 survey identified the need for guides, protocols, and standards to assist XPS users. One set of such guides has been published, and another is being assembled. Providing incremental bites of useful information is the goal of a series of papers on specific challenges to surface analysis with example solutions has been initiated as Notes and Insights papers in Surface and Interface Analysis. Examination of XPS-containing papers finds that information to establish the credibility and reproducibility of XPS results is often very incomplete. Unfortunately, ISO and ASTM standards require an amount of parameter reporting that seems excessive and unrealistic for many research publications. Initial approaches to develop and distribute a graded approach to parameter reporting are briefly described. Multiple efforts are underway to address the high rate of problems associated with photoelectron peak fitting. These include guides to peak fitting, guides to peak identification and fitting for specific elements, and the development of a peak fitting social network. The fitting social network is designed to facilitate interactions between new and experienced XPS users; analysts trying to fit XPS data (for publication or other reasons) can ask questions and establish dynamic conversations. Encouraging and enabling high-quality XPS analysis and reporting requires several different types of effort from all members of the surface and interface analysis community.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

A tetrahedral neptunium(V) complex

Neptunium is an actinide element sourced from anthropogenic production, and, unlike naturally abundant uranium, its coordination chemistry is not well developed in all accessible oxidation states. High-valent neptunium generally requires stabilization from at least one metal–ligand multiple bond, and departing from this structural motif poses a considerable challenge. Here we report a tetrahedral molecular neptunium(V) complex ([Np 5+ (NPC) 4 ][B(ArF 5 ) 4 ], 1-Np) (NPC = [NP t Bu(pyrr) 2 ] − ; t Bu = C(CH 3 ) 3 ; pyrr = pyrrolidinyl (N(C 2 H 4 ) 2 ); B(ArF 5 ) 4 = tetrakis(2,3,4,5,6-pentafluourophenyl)borate). Single-crystal X-ray diffraction, solution-state spectroscopy and density functional theory studies of 1-Np and the product of its proton-coupled electron transfer (PCET) reaction, 2-Np, demonstrate the unique bonding that stabilizes this reactive ion and establishes the thermochemical and kinetic parameters of PCET in a condensed-phase transuranic complex. The isolation of this four-coordinate, neptunium(V) complex reveals a fundamental reaction pathway in transuranic chemistry.

coordination chemistry↗

Remote Target Engagement System (RTES) Generation 4 ("Gen 4")

RTES is designed to provide teleoperation of remote weapon platform capability at defensive physical protection locations requiring active lethal capabilities for final access denial. Multiple operator consoles allow for coordinated engagement of many weapon platforms by one or more operator consoles. This results in faster response times, increased effectiveness, and increased survivability from removing response force from harm's way increasing survivability. This software is developed against DoD requirements and has been safety and functionally reviewed and approved for operational use. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525. SAND2021-1935 O

Klingler, KristopherR.↗

Rural Hybrid Generation & Microgrids: National Labs are Here to Help

Hear about the tools and technical assistance National Labs can offer to co-ops and other rural utilities seeking to build or enhance hybrid generation projects combining multiple technologies including wind, solar, battery storage, and others resources. These hybrids can deliver additional value by combining complimentary resources and by increasing reliability and resilience, including as part of a microgrid to serve a local load or community. Experienced cooperative and lab experts will share their perspectives on the opportunities there are to make use of their advanced modeling capabilities and expertise to scope, design, and implement these emerging hybrid solutions. Key takeaways for participants will include better understanding of the resilience benefits distributed hybrid systems provide, awareness of National Laboratory assistance programs and resources, tips for successful federal funding applications for these types of projects, and peer-to-peer learning through shared experiences and discussion.

14 SOLAR ENERGY↗

Role of chemical disorder on radiation-induced defect production and damage evolution in NiFeCoCr

Understanding chemical disorder in many concentrated solid solution alloys (CSAs) at the levels of electrons and atoms has attracted increasing attention as a path forward to reveal and identify underlying mechanisms for extraordinary mechanical properties and improved radiation tolerance. Single-phase NiFeCoCr CSA is a common base for many high-entropy alloys (HEAs) that have shown improved mechanical strength and radiation tolerance. In this study, defect production and damage evolution in NiFeCoCr under ion irradiation at room temperature to dose over 20 dpa are determined using ion channeling technique along both <100> and <110> directions utilizing multiple probing beam energies. The results obtained from the multi-axial and multi-energy channeling analysis are compared with those previously obtained for Ni crystals irradiated under similar conditions. The influence of chemical complexity on defect production and clustering at early-stage under room temperature irradiation up to dose of 1 dpa is discussed based on positron annihilation spectroscopy results. Defect structure evaluation in Ni and NiFeCoCr is also discussed based on transmission electron microscopy results over a prolonged irradiation at both room and elevated temperatures. Compared with chemically complex NiFeCoCr, larger dislocation loops thus less lattice strain are expected to form in pure Ni. Moreover, the role of chemical disorder in this CSA is also investigated based on ab initio calculations using large supercells. Finally, to understand the impact of chemical complexity effect on defect structure evolution, this integrated research effort attempts to link the relatively large charge redistribution due to difference in valence electron counts resulting from alloying different 3d transition metal elements, moderate lattice distortion arising from similar adaptable atomic size, and notable suppressed or delayed damage evolution in NiFeCoCr.

36 MATERIALS SCIENCE↗

Photonic ring resonator notch filters for astronomical OH line suppression

Photonic ring resonator arrays used as notch filters are a promising novel solution to improve the signal-to-noise ratio of ground-based astronomical observations by suppressing OH emission lines in the near-infrared (NIR) wavelength range (0.9-2.5 μm). We aim to fabricate a series of ring resonators connected by a waveguide, each with its resonance wavelength and full-width-half-maximum (FWHM) matched with one of the OH emission lines. The fundamental structure of ring resonator-based notch filters is a ring resonator evanescently coupled to a waveguide. Due to the optical loss of the ring resonator, the transmission of light through the waveguide will be suppressed at the resonance wavelength of the ring resonator. The degree of suppression can be controlled by varying the coupling strength between the waveguide and the ring resonator, while the resonance wavelength can be controlled by the geometry and size of the ring. However, since a single ring resonator has multiple modes each with a different wavelength separated by its free spectral range (FSR), the additional suppression at other wavelengths may undermine the signal. Therefore, the ideal FSR off the ring resonators needs to be larger than the measurement window. For this application, we aim the FSR to be 1 larger than 30 nm . Our initial measurement results revealed two major challenges for this application. First, the FSR of the ring resonators is limited by the bending loss of ring resonators. In a standard 220 nm silicon-on-insulator material platform, the FSR of ring resonators cannot exceed 30 nm; Second, due to the wafer thickness variation and fabrication inaccuracy, the achievable accuracy in the resonance wavelength of a fabricated single ring resonator is only ~1.5 nm. In this work, we report our novel solutions to the above two challenges. For the first one, we have developed a novel structure based on the Vernier effect to greatly raise the FSR of ring resonators. Serially coupled Vernier ring structures is a typical approach to increase the FSR without encountering bending loss, but it requires restrictive matching of the coupling and optical loss of the rings and any imperfect matching can result in even noisier signals. On the contrary, our structure, as shown in shown in Fig. 1, does not require the restrictive matching. The signals of two output ports in our structure marked in Fig. 1 will be added together to form the final output signal, and Fig. 2 shows the finite-difference time-domain (FDTD) simulation results of the final output signal. As can be seen, the FSR of the entire structure is well-beyond 60 nm with little noise at other wavelengths. For the second one, we have developed an easy-to-implement method to permanently tune the resonance wavelengths of ring resonators within 0.1 nm accuracy. The tuning mechanism is based on the property of hydrogen silsesquioxane (HSQ) resist. As a negative tone electron beam resist, HSQ upon exposure will cross-link and eventually form SiO2. The refractive 2 index of HSQ resist and formed SiO2 are slightly different , enabling the tuning. By having a thin layer of HSQ resist on top of ring resonator structures followed by SiO2 cladding, the resonance wavelengths of ring resonators can be post-tuned Advances in Optical Astronomical Instrumentation 2019, edited by Simon Ellis, Céline d'Orgeville, Downloaded From: https://www.spiedigitallibrary.org/conference-proceedings-of-spie on 23 Sep 2020 Terms of Use: https://www.spiedigitallibrary.org/terms-of-use through electron beam exposure. We have found that, within 5 nm tuning range, the resonance wavelength shift and dosage applied obeys a linear relationship. So a simple calibration beforehand can result in resonance wavelength tuning within 0.1 nm accuracy. The transmission spectra of a 3-ring device before and after tuning is shown in Fig. 3.

Astronomical optics↗

AquaMEND: Reconciling multiple impacts of salinization on soil carbon biogeochemistry

Soil salinization, exacerbated by climate change, poses a global threat to coastal ecosystem function and soil quality. Salinity influences carbon cycling through direct effects on microbial activity and indirect alterations to soil physicochemical properties including cation exchange, pH, and soil organic carbon availability. Current models inadequately represent these complexities, relying on linear reduction functions that overlook specific physicochemical changes induced by salinity. To address this gap, we propose an integrated model framework, AquaMEND, that combines microbial-explicit carbon decomposition and geochemical models. This model allows cation exchange and surface complexation processes to capture solute chemistry and nutrient availability in soils upon saltwater intrusion. Using response functions that capture salinity impacts on both salt-sensitive and salt-resistant microbial processes, AquaMEND simulates how the abiotic and biotic mechanisms work individually and collectively to regulate organic and inorganic pools and fluxes. Here, the parallel structure of aqueous and non-aqueous phases, together with microbial functions, result in a versatile model for solving dynamic coupling of organics, minerals and microbes under various environmental settings.

54 ENVIRONMENTAL SCIENCES↗

Time-series machine-learning error models for approximate solutions to parameterized dynamical systems

This work proposes a machine-learning framework for modeling the error incurred by approximate solutions to parameterized dynamical systems. In particular, we extend the machine-learning error models (MLEM) framework proposed in Ref. Freno and Carlberg (2019) to dynamical systems. The proposed Time-Series Machine-Learning Error Modeling (T-MLEM) method constructs a regression model that maps features – which comprise error indicators that are derived from standard a posteriori error-quantification techniques – to a random variable for the approximate-solution error at each time instance. The proposed framework considers a wide range of candidate features, regression methods, and additive noise models. We consider primarily recursive regression techniques developed for time-series modeling, including both classical time-series models (e.g., autoregressive models) and recurrent neural networks (RNNs), but also analyze standard non-recursive regression techniques (e.g., feed-forward neural networks) for comparative purposes. Finally, numerical experiments conducted on multiple benchmark problems illustrate that the long short-term memory (LSTM) neural network, which is a type of RNN, outperforms other methods and yields substantial improvements in error predictions over traditional approaches.

42 ENGINEERING↗