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DOE OSTI2024

First-principles study of the magneto-Raman effect in van der Waals layered magnets

Magneto-Raman spectroscopy has been used to study spin-phonon coupling in two-dimensional (2D) magnets. Raman spectra of CrI 3 show a strong dependence on the magnetic order within a layer and between the layers. Here we carry out the first systematic theoretical investigation of the magneto-Raman effect in 2D magnets by performing density functional theory calculations and developing a generalized polarizability model. Our first-principles simulations well reproduce experimental Raman spectra of CrI 3 with different magnetic states. The model reveals how the change of spin orientation in each layer is coupled to the layer’s vibration to induce or eliminate the spin-dependent anti-symmetric off-diagonal terms in the Raman tensor for altering the selection rules. We also uncover that the correlation between phonon modes and magnetic orders is a universal phenomenon, which should exist in other phonon modes and 2D magnets. Our predictive simulations and modeling are expected to guide the research in 2D magnets.

36 MATERIALS SCIENCE↗

DOE OSTI2019

First-principles studies of spin-phonon coupling in monolayer Cr 2 Ge 2 Te 6

Here, we perform systematic first-principles calculations to investigate the spin-phonon coupling (SPC) of Cr 2 Ge 2 Te 6 (CGT) monolayer (ML). It is found that the $E_g$ phonon mode at 211.8 cm –1 may have a SPC as large as 3.19 cm –1 , as it alters the direct exchange interaction across Cr-Cr pairs. The strength of SPC of the CGT ML can be decreased by an in-plane lattice stretch. These results provide useful insights for the understanding of SPC in two-dimensional magnetic semiconductors and may guide the design of spintronic and spin Seebeck materials and devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2024

Cyclic superconducting refrigerators using guided fluxon propagation

We propose cyclic refrigeration in solid state, employing a gas of magnetic field vortices in a type-II superconductor—also known as fluxons—as the cooling agent. Refrigeration cycles are realized by envisioning a racetrack geometry consisting of both adiabatic and isothermal arms, etched into a type-II superconductor. The guided propagation of fluxons in the racetrack is achieved by applying an external electrical current, in a Corbino geometry, through the sample. A gradient of magnetic field is set across the racetrack allowing one to adiabatically cool down and heat up the fluxons, which subsequently exchange heat with the cold and hot reservoirs, respectively. We characterize the steady state of refrigeration cycles thermodynamically for both s -wave and d -wave pairing symmetries, and present their figures of merit such as the cooling power delivered, and the coefficient of performance. Our cooling principle can offer significant cooling for on-chip microrefrigeration purposes, by locally cooling below the base temperatures achievable in a conventional dilution refrigerator. We estimate n W / m m 2 of cooling power per unit area assuming a tunnel coupling with ∼ M Ω µ m 2 specific resistance. Integrating the fluxon fridge to quantum circuits can enhance their coherence time by locally suppressing thermal fluctuations, and improve the efficiency of single photon detectors and charge sensors. Published by the American Physical Society 2024

Karmakar, Tathagata (ORCID:0000000187812017)↗

DOE OSTI2018

Quantifying gas generation from slurries used in fabrication of Si-containing electrodes for lithium-ion cells

The high capacity of silicon motivates its use as anode material for Li-ion batteries. While such Si-based electrodes are typically processed from aqueous slurries, elemental silicon is highly susceptible to hydrolysis, yielding flammable hydrogen gas as a byproduct. This gassing can present numerous safety and quality concerns for scaling up of the production. In this study we describe a method to quantify the volume of gases generated using the Archimedes principle. We show that the gas volumes are affected by various factors, which include the characteristics of the electrode slurry and attributes (particle size, surface coatings) of the silicon powders. A catalytic effect on gas evolution is observed when a lithiated polycarboxylate binder is present in the solution. On the other hand, negligible gas generation is observed in electrode slurries containing the N -methyl-2-pyrrolidone (NMP) solvent, which could be considered as an alternative to the water-based processes presently under consideration. Lastly, our study can be used as a guide for the development of slurry mixtures that can mitigate the safety concerns associated with the silicon-electrode fabrication process.

25 ENERGY STORAGE↗

DOE OSTI2018

Global Review of Site Suitability Conditions for the Geological Disposal of Radioactive Waste - 18136

The long-term safety of radioactive waste disposal is based on two key principles: (i) containment of the waste until the radioactivity has decreased to negligible levels, and (ii) the isolation of the waste packages in stable conditions that are conducive to maintaining the integrity of the containment for as long as required. In this paper, we review disposal site suitability criteria based on international best practices from geological disposal studies in crystalline and argillaceous rocks. Adopting social acceptability criteria in addition to safety-related criteria has been shown to be pivotal in obtaining community support. The reviewed criteria may be used as a guide to develop future site selection criteria for disposal of Australian intermediate level waste streams that are incompatible with surface disposal. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗

DOE OSTI2022

Electrically controllable chirality in a nanophotonic interface with a two-dimensional semiconductor

Chiral nanophotonic interfaces enable propagation direction-dependent interactions between guided optical modes and circularly dichroic materials. Electrical tuning of interface chirality would aid active, switchable non-reciprocity in on-chip optoelectronic and photonic circuitry, but remains an outstanding challenge. Here, in this paper, we report electrically controllable chirality in a nanophotonic interface with atomically thin monolayer tungsten diselenide (WSe 2 ). Titanium dioxide waveguides are directly fabricated on the surface of low-disorder, boron nitride-encapsulated WSe 2 . Following integration, photoluminescence from excitonic states into the waveguide can be electrically switched between balanced and directionally biased emission. The operational principle leverages the doping-dependent valley polarization of excitonic states in WSe 2 . Furthermore, the nanophotonic waveguide can function as a near-field source for diffusive exciton fluxes, which display valley and spin polarizations that are inherited from the interface chirality. Our versatile fabrication approach enables the deterministic integration of photonics with van der Waals heterostructures and could provide optical control over their excitonic and charge-carrier behaviour.

42 ENGINEERING↗

DOE OSTI2023

Fabricating Single Crystal Quantum Dot Solids

The central goal of this research program was to establish fundamental synthesis and processing principles to enable the fabrication of single crystal quantum dot solids (QDS) with programmable structure (i.e., hexagonal and square) and composition. Our approach towards that goal leveraged access to and experience with unique in-situ, multi-probe characterization techniques to understand and ultimately control the fundamental relationship between processing conditions and nucleation and growth of QDS. The program integrated synthesis and fabrication (Hanrath) with in-situ TEM analysis (Kourkoutis) and computational modeling (Clancy) to gain atomic-level insights into the underlying physical phenomena governing assembly and attachment and to guide the development of optimized processing methods. We have established a foundational understanding of physicochemical interactions of self-assembly at a fluid interface, superlattice structure transformation pathways, the critical role of disorder during the initial dimerization of colloidal quantum dot monomers, and the residual strain distribution within the inter-dot epitaxial bridge which hampers the formation of novel electronics states of the quantum dot solids. Collectively, these insights have contributed towards advancing the mechanistic understanding of the complex choreography of assembly and attachment as well as understanding what currently limits further advances in high-fidelity quantum dot solids and tiles.

36 MATERIALS SCIENCE↗

NASA NTRS1973

BUCLAP2: A computer program for instability analysis of laminated long plates subjected to combined inplane loads

This program description document describes the program structure and the design details of a CDC 6600 FORTRAN 4 digital computer program, BUCLAP2, which uses minimum energy principles to do an elastic stability analysis of curved and flat laminated rectangular long plates subjected to combined inplane normal and shear loads. Given the geometry, the material properties, and slected boundary conditions for the plate element, the program calculates the minimum buckling load for various wave lengths. The two parallel ends of the program calculates the minimum buckling load for various wave lengths. The two parallel ends of the long plate must be simply supported and arbitrary elastic boundary conditions may be imposed along either one or both external longitudinal sides. For guide to program use, see

Halstead, D. W.↗

NASA NTRS2016

Simulation Credibility: Advances in Verification, Validation, and Uncertainty Quantification

Decision makers and other users of simulations need to know quantified simulation credibility to make simulation-based critical decisions and effectively use simulations, respectively. The credibility of a simulation is quantified by its accuracy in terms of uncertainty, and the responsibility of establishing credibility lies with the creator of the simulation. In this volume, we present some state-of-the-art philosophies, principles, and frameworks. The contributing authors involved in this publication have been dedicated to advancing simulation credibility. They detail and provide examples of key advances over the last 10 years in the processes used to quantify simulation credibility: verification, validation, and uncertainty quantification. The philosophies and assessment methods presented here are anticipated to be useful to other technical communities conducting continuum physics-based simulations; for example, issues related to the establishment of simulation credibility in the discipline of propulsion are discussed. We envision that simulation creators will find this volume very useful to guide and assist them in quantitatively conveying the credibility of their simulations.

Simulation Credibility↗

DOE OSTI2018

Quantitative Principles for Precise Engineering of Sensitivity in Graphene Electrochemical Sensors

Abstract A major difficulty in implementing carbon‐based electrode arrays with high device‐packing density is to ensure homogeneous and high sensitivities across the array. Overcoming this obstacle requires quantitative microscopic models that can accurately predict electrode sensitivity from its material structure. Such models are currently lacking. Here, it is shown that the sensitivity of graphene electrodes to dopamine and serotonin neurochemicals in fast‐scan cyclic voltammetry measurements is strongly linked to point defects, whereas it is unaffected by line defects. Using the physics of point defects in graphene, a microscopic model is introduced that explains how point defects determine sensitivity. The predictions of this model match the empirical observation that sensitivity linearly increases with the density of point defects. This model is used to guide the nanoengineering of graphene structures for optimum sensitivity. This approach achieves reproducible fabrication of miniaturized sensors with extraordinarily higher sensitivity than conventional materials. These results lay the foundation for new integrated electrochemical sensor arrays based on nanoengineered graphene.

Wu, Ting↗

DOE OSTI2026

Data readiness pipeline patterns for scientific AI at scale: Insights from climate, fusion, life sciences, and materials

This article examines how data readiness for AI principles apply to large scientific datasets used to train foundation models. We analyze archetypal workflows across four representative domains—climate, nuclear fusion, life sciences, and materials—to identify common preprocessing patterns and domain‐specific constraints. We introduce a two‐dimensional readiness model that combines canonical preprocessing patterns with a five‐level operational readiness scale, both tailored to high‐performance computing (HPC) environments. This construct helps outline key challenges in transforming large‐scale scientific data into formats suitable for scalable AI training. Together, these dimensions form a conceptual maturity matrix that characterizes scientific data readiness and guides infrastructure development toward standardized, cross‐domain support for scalable and reproducible AI for science. Finally, we evaluate this maturity matrix in the context of case studies including ClimaX (climate), AFLOW (materials), OpenFold (proteomics), and DIII‐D fusion disruption‐prediction workflows, from which we distill lessons learned and provide recommendations to guide practitioners in developing robust AI‐readiness pipelines. Finally, we discuss remaining cross‐cutting challenges that persist across scientific domains.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

An Integrated Approach to Parameter Learning in Infinite-Dimensional Space

The availability of sophisticated modern physics codes has greatly extended the ability of domain scientists to understand the processes underlying their observations of complicated processes, but it has also introduced the curse of dimensionality via the many user-set parameters available to tune. Many of these parameters are naturally expressed as functional data, such as initial temperature distributions, equations of state, and controls. Thus, when attempting to find parameters that match observed data, being able to navigate parameter-space becomes highly non-trivial, especially considering that accurate simulations can be expensive both in terms of time and money. Existing solutions include batch-parallel simulations, high-dimensional, derivative-free optimization, and expert guessing, all of which make some contribution to solving the problem but do not completely resolve the issue. In this work, we explore the possibility of coupling together all three of the techniques just described by designing user-guided, batch-parallel optimization schemes. Our motivating example is a neutron diffusion partial differential equation where the time-varying multiplication factor serves as the unknown control parameter to be learned. We find that a simple, batch-parallelizable, random-walk scheme is able to make some progress on the problem but does not by itself produce satisfactory results. After reducing the dimensionality of the problem using functional principal component analysis (fPCA), we are able to track the progress of the solver in a visually simple way as well as viewing the associated principle components. This allows a human to make reasonable guesses about which points in the state space the random walker should try next. Thus, by combining the random walker's ability to find descent directions with the human's understanding of the underlying physics, it is possible to use expensive simulations more efficiently and more quickly arrive at the desired parameter set.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2017

Cations in Octahedral Sites: A Descriptor for Oxygen Electrocatalysis on Transition-Metal Spinels

Exploring efficient and low-cost electrocatalysts for the oxygen-reduction reaction (ORR) and oxygen-evolution reaction (OER) is critical for developing renewable energy technologies such as fuel cells, metal–air batteries, and water electrolyzers. A rational design of a catalyst can be guided by identifying descriptors that determine its activity. Here, a descriptor study on the ORR/OER of spinel oxides is presented. With a series of MnCo 2 O 4 , the Mn in octahedral sites is identified as an active site. This finding is then applied to successfully explain the ORR/OER activities of other transition-metal spinels, including Mn x Co 3-x O 4 (x = 2, 2.5, 3), Li x Mn 2 O 4 (x = 0.7, 1), XCo 2 O 4 (X = Co, Ni, Zn), and XFe 2 O 4 (X = Mn, Co, Ni). A general principle is concluded that the e g occupancy of the active cation in the octahedral site is the activity descriptor for the ORR/OER of spinels, consolidating the role of electron orbital filling in metal oxide catalysis.

36 MATERIALS SCIENCE↗

DOE OSTI2017

A historical survey of algorithms and hardware architectures for neural-inspired and neuromorphic computing applications

In this study, biological neural networks continue to inspire new developments in algorithms and microelectronic hardware to solve challenging data processing and classification problems. Here in this research, we survey the history of neural-inspired and neuromorphic computing in order to examine the complex and intertwined trajectories of the mathematical theory and hardware developed in this field. Early research focused on adapting existing hardware to emulate the pattern recognition capabilities of living organisms. Contributions from psychologists, mathematicians, engineers, neuroscientists, and other professions were crucial to maturing the field from narrowly-tailored demonstrations to more generalizable systems capable of addressing difficult problem classes such as object detection and speech recognition. Algorithms that leverage fundamental principles found in neuroscience such as hierarchical structure, temporal integration, and robustness to error have been developed, and some of these approaches are achieving world-leading performance on particular data classification tasks. Additionally, novel microelectronic hardware is being developed to perform logic and to serve as memory in neuromorphic computing systems with optimized system integration and improved energy efficiency. Key to such advancements was the incorporation of new discoveries in neuroscience research, the transition away from strict structural replication and towards the functional replication of neural systems, and the use of mathematical theory frameworks to guide algorithm and hardware developments.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

The center for nonlinear studies: A personal history

The Center for Nonlinear Studies (CNLS) was an integral part of my scientific career starting as a Postdoctoral Fellow in 1983 up to my tenure as CNLS Director from 2004 to 2015. As such, I experienced a number of scientific phases of CNLS through almost four decades of foundation, evolution, and transition. Throughout this entire interval, the inspiration and influence of David Campbell guided my way. A proper history of CNLS encompassing all of the many contributors to the CNLS story is beyond my means or purpose here. Instead, I present the history as I experienced it. I emphasize the main scientific accomplishments achieved at CNLS over more than 40 years, but I will also attempt to describe and quantify the attributes that made and continue to make the Center for Nonlinear Studies a special institution of remarkable impact and longevity. Throughout its existence, CNLS owes much to the enduring legacy of David Campbell who laid down the foundations and operating principles that have made it so successful.

Ecke, Robert E. (ORCID:0000000177725876)↗

DOE OSTI2025

Data Structure Alchemy

In an increasingly more data-driven world, the project set out to uncover the first principles of data-structure design, chart the immense design space they form, and build automation that can synthesize an optimal structure, or even a whole storage engine, for any given workload, hardware platform, and cost target. Data structures are at the center of every computational system and are directly responsible for its performance. Two core technical thrusts were defined: 1) Mapping design spaces for key data-centric abstractions (filters, hash functions, storage-engine layouts, neural-network topologies, blockchain protocols, image layouts, etc.). 2) Developing search & synthesis algorithms, initially analytical cost models, later neural-guided bi-level optimisers that navigate sextillions of candidate designs in seconds and materialise the best one as ready‐to-run code. This report distills the key insights, accomplishments, and impact.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2021

Numerical Modelling of the Optical Stochastic Cooling Experiment at IOTA

A proof-of-principle optical-stochastic cooling (OSC) experiment is currently in its commissioning phase at the Fermilab’s IOTA ring. In support of this experiment, we recently implemented an OSC element in the ELEGANT tracking program. The model, based on a semi-analytic description of OSC [*], supports the simulation of a large number of macroparticles (10⁴-10⁶) over many turns (10⁶). This paper showcases the simulation capabilities to investigate the beam dynamics in the presence of cooling (or self-interacting radiation field in general) and quantify the impact of various sources of error (e.g. transverse and phase jitter), guide data analysis.

43 PARTICLE ACCELERATORS↗

DOE OSTI2021

Machine Learning Assisted Safety Modeling and Analysis of Advanced Reactors

With the advances in computational power and numerical methods, analysts can now rely on first-principle simulations to predict ultra-fine details in a variety of applications. Advances in machine learning (ML) have produced algorithms that can now learn high-level abstractions via hierarchical models. This project aims to leverage advances in ML techniques and the available high-resolution simulation data to develop a novel modeling and simulation (M\&S) methodology for reactor safety analysis. While application-agnostic ML techniques are available, complex physics constraints need to be incorporated into ML techniques to build ML-based closures for computationally efficient predictive simulations. This project intends to develop a physics-guided data-driven multi-scale methodology for M\&S of advanced reactors. The project focuses on thermal fluid (T/F) phenomena, which play major roles in advanced reactor safety. Specifically, we propose a data-driven coarse-mesh turbulence model based on local flow features for the transient analysis of thermal mixing and stratification in a sodium-cooled fast reactor (SFR). The model has a coarse-mesh setup to ensure computational efficiency, while it is trained by fine-mesh computational fluid dynamics (CFD) data with Reynolds-averaged Navier-Stokes (RANS) turbulence model to ensure accuracy. Three different neural networks are developed and tested for loss-of-flow transients in the hot pool of SFR, i.e. the densely connected convolutional neural network (DCNN), long-short-term-memory network based on proper orthogonal decomposition (POD-LSTM), and the DCNN informed by LSTM (DCNN-LSTM). The performances of these three neural networks are evaluated based on baseline models. The DCNN-LSTM model has been chosen for further hyperparameter optimization. Furthermore, based on a simplified two-dimensional case, uncertainty quantification (UQ) of the developed ML-based closure are investigated with three methods, i.e. Monte Carlo dropout, deep ensemble, and Bayesian neural network. The developed ML-based turbulent viscosity closure relation based on deep ensemble is then integrated into the system analysis module SAM and serves as a term in the conservation equations. Such a SAM-ML based procedure guarantees that the obtained results are consistent with the physical constraints of the thermal-fluid system. The SAM-ML simulation on the same loss-of-flow transient showed comparable accuracy with the CFD simulation but with a much coarser mesh setup. Last but not least, the ML-based closure improvement with the support of higher-fidelity data from large eddy simulation (LES) is discussed. As a first step towards this direction, a baseline LES simulation is performed to obtain comparable data with RANS results. Based on the early results, future investigation on further improving the ML-based closure is discussed. We believe the developed approach that combines scientific machine learning with nuclear system analysis code can benefit the advanced reactor community as more accurate safety analyses will better characterize reactor safety margins and reduce licensing efforts.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗