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DOE OSTI2024

Advancing quantitative models of soil microbiology, ecology, and biochemistry

Rapid advances in soil biology are increasingly reflected in mathematical models. From molecular to global scales, models contribute to fundamental understanding and prediction of critical soil processes, such as carbon sequestration. Recent models explicitly incorporate microbial control over soil carbon and nutrient cycling, an approach that offers greater biological realism but raises new challenges in model parameterization and stability. Although they have proliferated at community and ecosystem scales, microbial-explicit models have yet to be incorporated into most Earth system models. Applying soil models across scales requires integration with data to support parameterization and validation using approaches such as Bayesian data assimilation. In conclusion, to catalyze future progress, we recommend breaking down barriers between modeling and empirical disciplines while broadening access to the computational infrastructure that supports soil biological modeling.

Xie, Wally↗

DOE OSTI2023

Optimizing Phase Change Composite Thermal Energy Storage Using the Thermal Ragone Framework

The thermal Ragone framework can be applied to the design of thermal energy storage heat exchangers to identifying relationships between their power requirements and energy capabilities. This presentation focuses on the design of planar thermal energy storage heat exchangers with phase change materials and thermal conductivity additives. Finite-difference models were used to optimize thermal storage heat exchanger designs focusing on key design parameters of conductivity additive volume fraction, spacing between tubes, and the phase transition temperatures. Design trends were developed for optimal designs based on varying thermal power requirements. To aid in future device design, models of reduced complexity were developed with the ability to predict optimal designs with computational speeds up to 5,000 times faster than finite-difference models.

ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION,↗

DOE OSTI2017

Empowering Intensity Modulated Proton Therapy Through Physics and Technology: An Overview

Considering the clinical potential of protons attributable to their physical characteristics, interest in proton therapy has increased greatly in this century, as has the number of proton therapy installations. Until recently, passively scattered proton therapy was used almost entirely. Notably, the overall clinical results to date have not shown a convincing benefit of protons over photons. A rapid transition is now occurring with the implementation of the most advanced form of proton therapy, intensity modulated proton therapy (IMPT). IMPT is superior to passively scattered proton therapy and intensity modulated radiation therapy (IMRT) dosimetrically. However, numerous limitations exist in the present IMPT methods. In particular, compared with IMRT, IMPT is highly vulnerable to various uncertainties. In this overview we identify three major areas of current limitations of IMPT: treatment planning, treatment delivery, and motion management, and discuss current and future efforts for improvement. For treatment planning, we need to reduce uncertainties in proton range and in computed dose distributions, improve robust planning and optimization, enhance adaptive treatment planning and delivery, and consider how to exploit the variability in the relative biological effectiveness of protons for clinical benefit. The quality of proton therapy also depends on the characteristics of the IMPT delivery systems and image guidance. Efforts are needed to optimize the beamlet spot size for both improved dose conformality and faster delivery. For the latter, faster energy switching time and increased dose rate are also needed. Real-time in-room volumetric imaging for guiding IMPT is in its early stages with cone beam computed tomography (CT) and CT-on-rails, and continued improvements are anticipated. In addition, imaging of the proton beams themselves, using, for instance, prompt γ emissions, is being developed to determine the proton range and to reduce range uncertainty. With the realization of the advances described above, we posit that IMPT, thus empowered, will lead to substantially improved clinical results.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2025

Factorization for jet production in heavy-ion collisions

We develop an Effective Field Theory approach for jet observables in heavy-ion collisions, where the jet is treated as an open quantum system interacting with a hot and dense QCD medium. Within this framework, we derive a novel factorization formula for inclusive jet production, expressed as a series expansion with an increasing number of radiating subjet functions that encode forward scattering with the Quark-Gluon Plasma, convolved with perturbative matching coefficients. This work provides a systematic framework for computing jet observables at higher order and understanding their non-perturbative aspects, paving the way for future applications in heavy-ion phenomenology.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2019

Trends in Solid Adsorbent Materials Development for CO 2 Capture

A review summarizing trends in materials development for CO 2 adsorption focusing on recent studies is given. Trends in computational investigations along with experimental findings are covered to find promising candidates in light of practical challenges imposed by process economics. Topics covered here include: introduction (climate change, CO 2 emissions mitigation, overview of classical computational CO 2 adsorption studies); CO 2 capture materials (C based materials [activated C {AC}, ordered porous C, C fiber, graphene], SiO 2 /Al 2 O 3 /zeolite [SiO 2 , Al 2 O 3 and nanoclays, zeolite], porous cryst. solids [metal-org. frameworks (MOF) {metal type and open metal sites, functionalized MOF, mol. sieves, MOF computational modeling, MOF databases}, zeolite imidazolate frameworks, covalent org. frameworks and polymers], metal oxides); and conclusions and future perspectives.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Empowering a qudit-based quantum processor by traversing the dual bosonic ladder

Abstract High-dimensional quantum information processing has emerged as a promising avenue to transcend hardware limitations and advance the frontiers of quantum technologies. Harnessing the untapped potential of the so-called qudits necessitates the development of quantum protocols beyond the established qubit methodologies. Here, we present a robust, hardware-efficient, and scalable approach for operating multidimensional solid-state systems using Raman-assisted two-photon interactions. We then utilize them to construct extensible multi-qubit operations, realize highly entangled multidimensional states including atomic squeezed states and Schrödinger cat states, and implement programmable entanglement distribution along a qudit array. Our work illuminates the quantum electrodynamics of strongly driven multi-qudit systems and provides the experimental foundation for the future development of high-dimensional quantum applications such as quantum sensing and fault-tolerant quantum computing.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2015

MIL-101(Fe) as a lithium-ion battery electrode material: a relaxation and intercalation mechanism during lithium insertion

The electrochemical performance of a MIL-101(Fe) metal–organic framework (MOF) as a lithium ion battery electrode is reported for the first time. Iron metal centers can be electrochemically activated. The Fe 3+ /Fe 2+ redox couple is electrochemically active, but not reversible over many cycles. A comparison between ex situ and in operando X-ray absorption spectroscopy (XAS) on the Fe K-edge is presented. Our results indicate that the capacity fade is related to a time dependent, irreversible oxidation of Fe 2+ to Fe 3+ . These results are key in proving the importance of in operando XAS measurements. The MOF side reaction with an electrolyte has been computationally modeled. These results provide further insights on the mechanism responsible for the MOF lack of reversibility. Future guidelines for improving the reversibility of MOFs used as electrodes in Li-ion batteries based on the fine-tuning of the electronic structure of the material are proposed.

25 ENERGY STORAGE↗

DOE OSTI2019

Data automation at light sources

Rapidly growing data volumes at light sources demand increasingly automated data collection, distribution, and analysis processes, in order to enable new scientific discoveries while not overwhelming finite human capabilities. We present here the case for automating and outsourcing light source science using cloud-hosted data automation and enrichment services, institutional computing resources, and high- performance computing facilities to provide cost-effective, scalable, and reliable implementations of such processes. We discuss three specific services that accomplish these goals for data distribution, automation, and transformation. In the first, Globus cloud-hosted data automation services are used to implement data capture, distribution, and analysis workflows for Advanced Photon Source and Advanced Light Source beamlines, leveraging institutional storage and computing. In the second, such services are combined with cloud-hosted data indexing and institutional storage to create a collaborative data publication, indexing, and discovery service, the Materials Data Facility (MDF), built to support a host of informatics applications in materials science. The third integrates components of the previous two projects with machine learning capabilities provided by the Data and Learning Hub for science (DLHub) to enable on-demand access to machine learning models from light source data capture and analysis workflows, and provides simplified interfaces to train new models on data from sources such as MDF on leadership scale computing resources. We draw conclusions about best practices for building next-generation data automation systems for future light sources.

Blaiszik, Ben↗

DOE OSTI2013

Extending BEAMS to incorporate correlated systematic uncertainties

New supernova surveys such as the Dark Energy Survey, Pan-STARRS and the LSST will produce an unprecedented number of photometric supernova candidates, most with no spectroscopic data. Avoiding biases in cosmological parameters due to the resulting inevitable contamination from non-Ia supernovae can be achieved with the BEAMS formalism, allowing for fully photometric supernova cosmology studies. Here we extend BEAMS to deal with the case in which the supernovae are correlated by systematic uncertainties. The analytical form of the full BEAMS posterior requires evaluating 2{sup N} terms, where N is the number of supernova candidates. This 'exponential catastrophe' is computationally unfeasible even for N of order 100. We circumvent the exponential catastrophe by marginalising numerically instead of analytically over the possible supernova types: we augment the cosmological parameters with nuisance parameters describing the covariance matrix and the types of all the supernovae, τ{sub i}, that we include in our MCMC analysis. We show that this method deals well even with large, unknown systematic uncertainties without a major increase in computational time, whereas ignoring the correlations can lead to significant biases and incorrect credible contours. We then compare the numerical marginalisation technique with a perturbative expansion of the posterior based on the insight that future surveys will have exquisite light curves and hence the probability that a given candidate is a Type Ia will be close to unity or zero, for most objects. Although this perturbative approach changes computation of the posterior from a 2{sup N} problem into an N{sup 2} or N{sup 3} one, we show that it leads to biases in general through a small number of misclassifications, implying that numerical marginalisation is superior.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2024

Circumventing data imbalance in magnetic ground state data for magnetic moment predictions

Abstract Magnetic materials play a crucial role in the transition to more sustainable forms of energy and electric vehicles. There is an anticipated shortage in magnetic materials in the future, and as a result there is an urgent need to discover and design new magnetic materials. Computational magnetic material design using density functional theory is daunting because of the challenge in identifying magnetic ground states from a combinatorially large set of possibilities. Machine learning offers a path forward by enabling efficient surrogate models that can more readily enumerate these states, but there is a dearth of training data available, and what is available tends to be imbalanced with too much non-magnetic data. In this work we show that the discrete and previously tackled data imbalance that exists at the level of the magnetic ordering leads to an imbalanced continuous distribution with many zeros when the data is unraveled at the atomic magnetic moment level, which subsequently leads to models with low accuracy for magnetic properties. We mitigate this by using a two-part model framework. Our scheme is able to classify atoms into magnetic and non-magnetic with an F1 score and Matthew’s correlation coefficient (MCC) of ~91% and then to provide an implicit embedding representation that maps directly onto the magnitude of the magnetic moment with a mean absolute error of 0.1 μ B . Beyond screening for new magnetic materials, we demonstrate an additional practical use case of our scheme: the provision of good initial guesses for magnetic moments in first-principles electronic relaxations. Such initialization is shown to lead to faster convergence to configurations that lie closer to the ground state.

Computer Science↗

DOE OSTI2025

Lighting up yeast: overview of optogenetics in yeast and their applications to yeast biotechnology

Abstract Optogenetics is an empowering technology that uses light-responsive proteins to control biological processes. Because of its genetic tractability, abundance of genetic tools, and robust culturing conditions, Saccharomyces cerevisiae has served for many years as an ideal platform in which to study, develop, and apply a wide range of optogenetic systems. In many instances, yeast has been used as a steppingstone in which to characterize and optimize optogenetic tools to later be deployed in higher eukaryotes. More recently, however, optogenetic tools have been developed and deployed in yeast specifically for biotechnological applications, including in nonconventional yeasts. In this review, we summarize various optogenetic systems responding to different wavelengths of light that have been demonstrated in diverse yeast species. We then describe various applications of these optogenetic tools in yeast, particularly in metabolic engineering and recombinant protein production. Finally, we discuss emerging applications in yeast cybergenetics—the interfacing of yeast and computers for closed-loop controls of yeast bioprocesses—and the potential impact of optogenetics in other future biotechnological applications.

Biotechnology & Applied Microbiology↗

DOE OSTI2026

Tensor network simulations of quasi-GPDs in the massive Schwinger model

Generalized parton distribution functions (GPDs) are off-diagonal light-cone matrix elements that encode the internal structure of hadrons in terms of quark and gluon degrees of freedom. In this work, we present the first nonperturbative study of quasi-GPDs in the massive Schwinger model, quantum electrodynamics in 1+1 dimensions (QED 2 ), within the Hamiltonian formulation of lattice field theory. Quasidistributions are spatial correlation functions of boosted states, which approach the relevant light-cone distributions in the luminal limit. Using tensor networks, we prepare the first excited state in the strongly coupled regime and boost it to close to the light-cone on lattices of up to 400 lattice sites. We compute both quasiparton distribution functions and, for the first time, quasi-GPDs, and study their convergence for increasingly boosted states. In addition, we perform analytic calculations of GPDs in the two-particle Fock-space approximation and in the Reggeized limit, providing qualitative benchmarks for the tensor network results. Our analysis establishes computational benchmarks for accessing partonic observables in low-dimensional gauge theories, offering a starting point for future extensions to higher dimensions, non-Abelian theories, and quantum simulations.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2018

Development of a Spouted Bed Reactor for Chemical Looping Combustion

We have investigated a novel gas/solid contacting configuration for chemical looping combustion (CLC) with potential operating benefits. CLC configurations are typically able to achieve high fuel conversion efficiencies at the expense of high operating costs and low system reliability. The spouted fluid bed (SB) was identified as an improved reactor configuration for CLC, since it typically exhibits high heat transfer rates and offers the ability to use lower gas flows for material movement compared to bubbling beds (BB). Multiphase Flow with Interphase eXchanges (MFIX) software was used to establish a spouted fluid bed reactor design. An experimental setup was built to supplement the model. The experimental setup was also modified for testing under high temperature, reacting conditions (1073–1273 K). The setup was operated in either a spouted fluid bed or a bubbling bed regime to compare the performance attributes of each. Results for the reactor configurations are presented for CLC using a mixture of carbon monoxide and hydrogen as fuel. Compared to the bubbling bed, the spouted fluid bed reactor achieved an equivalent or better fuel conversion at a lower pressure drop over the material bed. The spouted fluid bed design represents a viable configuration to improve gas/solid contacting for efficient fuel conversion, lower energy requirements for material movement and increase operational robustness for CLC. The research laid the groundwork for future research into a multi-phase reacting flow CLC system. The system will be developed from computational fluid dynamic modeling and pilot-scale testing to expedite the development of CLC technologies.

Energy & Fuels↗

DOE OSTI2023

An Analytic Formula for Entraining CAPE in Midlatitude Storm Environments

Abstract This article introduces an analytic formula for entraining convective available potential energy (ECAPE) with an entrainment rate that is determined directly from an environmental sounding, rather than prescribed by the formula user. Entrainment is connected to the background environment using an eddy diffusivity approximation for lateral mixing, updraft geometry assumptions, and mass continuity. These approximations result in a direct correspondence between the storm-relative flow and the updraft radius and an inverse scaling between the updraft radius squared and entrainment rate. The aforementioned concepts, combined with the assumption of adiabatic conservation of moist static energy, yield an explicit analytic equation for ECAPE that depends entirely on state variables in an atmospheric profile and a few constant parameters with values that are established in past literature. Using a simplified Bernoulli-like equation, the ECAPE formula is modified to account for updraft enhancement via kinetic energy extracted from the cloud’s background environment. CAPE and ECAPE can be viewed as predictors of the maximum vertical velocity w max in an updraft. Hence, these formulas are evaluated using w max from past numerical modeling studies. Both of the new formulas improve predictions of w max substantially over commonly used diagnostic parameters, including undiluted CAPE and ECAPE with a constant prescribed entrainment rate. The formula that incorporates environmental kinetic energy contribution to the updraft correctly predicts instances of exceedance of by w max , and provides a conceptual explanation for why such exceedance is rare among past simulations. These formulas are potentially useful in nowcasting and forecasting thunderstorms and as thunderstorm proxies in climate change studies. Significance Statement Substantial mixing occurs between the upward-moving air currents in thunderstorms (updrafts) and the surrounding comparatively dry environmental air, through a process called entrainment. Entrainment controls thunderstorm intensity via its diluting effect on the buoyancy of air within updrafts. A challenge to representing entrainment in forecasting and predictions of the intensity of updrafts in future climates is to determine how much entrainment will occur in a given thunderstorm environment without a computationally expensive high-resolution simulation. To address this gap, this article derives a new formula that computes entrainment from the properties of a single environmental profile. This formula is shown to predict updraft vertical velocity more accurately than past diagnostics, and can be used in forecasting and climate prediction to improve predictions of thunderstorm behavior and impacts.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2011

Packaging strategies for printed circuit board components. Volume I, materials & thermal stresses.

Decisions on material selections for electronics packaging can be quite complicated by the need to balance the criteria to withstand severe impacts yet survive deep thermal cycles intact. Many times, material choices are based on historical precedence perhaps ignorant of whether those initial choices were carefully investigated or whether the requirements on the new component match those of previous units. The goal of this program focuses on developing both increased intuition for generic packaging guidelines and computational methodologies for optimizing packaging in specific components. Initial efforts centered on characterization of classes of materials common to packaging strategies and computational analyses of stresses generated during thermal cycling to identify strengths and weaknesses of various material choices. Future studies will analyze the same example problems incorporating the effects of curing stresses as needed and analyzing dynamic loadings to compare trends with the quasi-static conclusions.

36 MATERIALS SCIENCE↗

DOE OSTI2019

Enhancing TREAT’s Pulsing Capabilities Using TREAT Upgrade Fuel

The Transient Reactor Test Facility (TREAT) at Idaho National Laboratory is an air-cooled, thermal, heterogeneous facility used to test reactor materials in simulated accident conditions by inducing fission heating with intense neutron pulses. TREAT operated from 1959 until 1994 with the primary goal of testing fast reactor fuels and was brought back online in 2017 to re-establish DOE's nuclear fuels transient testing capabilities. Beginning in the late 1970s, Idaho National Laboratory worked in conjunction with other organizations to increase TREAT's capability for in-pile testing. While these upgrades had not been implemented by the time the reactor was put into standby mode, new assemblies and graphite-urania fuel blocks with increased uranium concentrations had already been designed and fabricated. This project, as part of Idaho National Laboratory's Department of Reactor Physics, modeled the TREAT Upgrade (TU) fuel assemblies in MCNP and implemented them in an existing TREAT model. Calculations were performed using INL's high performance computer to find a critical combination of TU and standard fuel and this geometry's excess reactivity. Future work will include calculating a power coupling factor in the experiment, determining power peaking factors throughout the core, and assessing how much reactivity would be added by replacing the Inconel-625 cladding with silicon carbide.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2019

Enhancing TREAT's pulsing abilities using TREAT upgrade fuel. SULI Intern Report

The Transient Reactor Test Facility (TREAT) at Idaho National Laboratory is an air-cooled, thermal, heterogeneous facility used to test reactor materials in simulated accident conditions by inducing fission heating with intense neutron pulses. TREAT operated from 1959 until 1994 with the primary goal of testing fast reactor fuels and was brought back online in 2017 to re-establish DOE’s nuclear fuels transient testing capabilities. Beginning in the late 1970s, Idaho National Laboratory worked in conjunction with other organizations to increase TREAT’s capability for in-pile testing. While these upgrades had not been implemented by the time the reactor was put into standby mode, new assemblies and graphite-urania fuel blocks with increased uranium concentrations had already been designed and fabricated. This project, as part of Idaho National Laboratory’s Department of Reactor Physics, modeled the TREAT Upgrade (TU) fuel assemblies in MCNP and implemented them in an existing TREAT model. Calculations were performed using INL’s high performance computer to find a critical combination of TU and standard fuel and this geometry’s excess reactivity. Future work will include calculating a power coupling factor in the experiment, determining power peaking factors throughout the core, and assessing how much reactivity would be added by replacing the Inconel-625 cladding with silicon carbide.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2024

Use Case-Informed Framework for Utility Cloud Migration

This white paper presents a comprehensive methodology for assessing utilities’ cloud postures and frameworks. It aims to produce a roadmap and strategy for a cloud-enabled grid future, providing guidance for integrators, asset owners, and operators. Instead of offering a yes or no answer for cloud implementation, this framework offers strategic guidance on responsibly preparing for and deploying cloud solutions. This paper delves into cloud-service models pertinent to the electric sector, dissecting the shared responsibility model and elucidating what on-premise infrastructure as a service (IaaS), platform as a service (PaaS), and software as a service (SaaS) entail. A pivotal consideration within the context of the shared responsibility model is the allocation of responsibility for foundational security aspects—a decision that will be informed by a comprehensive risk assessment. The ensuing discussion will present a checklist of certifications necessary for a secure cloud transition, equipping utilities with the knowledge to navigate this digital transformation with confidence and with a strategic roadmap. Furthermore, the paper outlines gaps in understanding the U.S. government’s role in shaping technology development and responsible use. Its purpose is to aid decision-making by offering support for risk-informed solutions that benefit those managing assets and operating in the cloud environment. The primary objective is to enhance the resilience and future readiness of a decarbonized electric grid, with cloud solutions as one viable option. The paper synthesizes information on current and future grid architectures and applications, considering both conservative and progressive energy transitions, along with scalable and distributed computing considerations.

24 POWER TRANSMISSION AND DISTRIBUTION↗