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DOE OSTI2024

Initial Development of Fusion Magnet Simulation Capabilities for Performance and Safety Evaluation Using the MOOSE Framework

Fusion energy holds the promise of being a transformative technology as a carbon-neutral, sustainable source of energy. Whole device modeling and the development of fusion digital twins will be increasingly important for emerging fusion device concepts at both national laboratories and within the commercial fusion industry. However, meeting the challenge of whole device modeling of fusion energy devices requires robust, multiphysics, multiscale modeling and simulation technologies capable of running on large-scale supercomputers. Detailed analysis of individual systems at-scale is also required to ensure safe and efficient operation as well as provide the safety basis for future device designs and licensing activities. In a tokamak, toroidal and poloidal magnets confine and shape the fusion plasma to promote the fusion reaction. High plasma temperatures and high magnetic field requirements in modern design concepts (leading to high amounts of energy stored within each magnet) impose electrical, thermal, and mechanical loads on the magnet components, which in turn impacts the safety considerations of the magnet and their supporting systems. Idaho National Laboratory (INL) has a history of working in this space, including development and benchmarking of the Magnetic System Circuitry Analysis Program (MSCAP) and Magnet Arcing (MAGARC) codes to study magnet quench events; notably, MAGARC was used to study quenching during the ITER Engineering Design Activity. However, these legacy codes and capabilities are not parallel and scalable, and new tools are required for future advances in this area, which leads to the INL-developed Multiphysics Object-Oriented Simulation Environment (MOOSE) framework. Developed originally for fission reactor systems under United States Department of Energy, Office of Nuclear Energy modeling and simulation programs, the MOOSE framework has been well-suited to multiscale, multiphysics modeling and simulation needs for nuclear systems. The framework is open-source, well-tested, under continuous development and deployment, and developed to a Nuclear Quality Assurance, Level 1 software quality standard. MOOSE has also been used in the fusion space previously in several projects: INL’s Tritium Migration Analysis Program, Version 8 (TMAP8) for tritium migration, UK Atomic Energy Authority’s A Unified Resource for OpenMC (fusion) Reactor Applications (AURORA) code for fusion thermo-mechanical and neutronics analysis, and Argonne National Laboratory’s Cardinal for high-fidelity computational fluid dynamics and neutronics. However, to model superconducting magnets, several MOOSE enhancements are required: additions to the current MOOSE electromagnetic capabilities, new material libraries for superconductors of interest (such as YBCO), as well as fusion-specific models for thermo-mechanics. This talk will discuss initial development activities to build these capabilities in MOOSE, focusing on initial validation and benchmarking activities. Proposed coupling workflows and future work to support the simulation of fusion magnets and magnet structural assemblies for performance and safety evaluation in MOOSE will also be discussed.

70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

A Physics-Informed Reinforcement Learning Framework for Economic-Thermal Co-Optimization of Crypto Mining Data Centers: Preprint

The rapid expansion of cryptocurrency mining has created a new class of high-density data centers characterized by extreme thermal flux and high sensitivity to volatile economic markets. Traditional thermal management strategies, typically reliant on rule-based control, maintain static setpoints that fail to account for fluctuating electricity prices and cryptocurrency values - factors critical to mining profitability. To address this, we present a physics-informed reinforcement learning (PIRL) framework for economic-thermal co-optimization in crypto mining data centers. This framework consists of a proximal policy optimization (PPO) agent, a virtual testbed powered by high-fidelity physics-based models, and an interactive frontend dashboard. The PPO agent is trained using the virtual testbed and strict hardware safety limits. This physics-informed approach allows the agent to learn a stochastic policy that dynamically balances mining revenue against operational costs by co-optimizing HVAC cooling setpoints and IT computational hashrate. The simulation results demonstrate that the integrated framework achieved an 8.62% increase in net operational profit compared to traditional baseline strategies while strictly adhering to safety-critical temperature constraints (coolant supply temperature < 32 degrees C). This work provides a scalable template for the deployment of reinforcement learning in mission critical facilities where economic volatility and physical safety must be managed simultaneously.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2024

Predicting Partial Atomic Charges in Metal–Organic Frameworks: An Extension to Ionic MOFs

Molecular simulation is an invaluable tool to predict and understand the usage of metal–organic frameworks (MOFs) for gas storage and separation applications. Accurate partial atomic charges, commonly obtained from density functional theory (DFT) calculations, are often required to model the electrostatic interactions between the MOF and adsorbates, especially when the adsorbates have dipole or quadrupole moments, such as water and CO 2 . Machine learning (ML) models have been previously employed to predict partial charges and avoid the computational cost associated with DFT calculations. However, previous ML models suffer from small training data sets, which limit their scope of application. In this work, we introduce two novel machine learning models, PACMOF2-neutral and PACMOF2-ionic, aimed at predicting the density-derived electrostatic and chemical (DDEC6) partial atomic charges for both neutral and ionic MOFs. These models not only yield DFT-level accuracy at a fraction of the computational cost but also demonstrate a remarkable improvement in prediction of adsorption, as validated with grand canonical Monte Carlo simulations. Furthermore, the robustness and fast computational time of the PACMOF2 models, along with their transferability to other porous materials such as covalent organic frameworks and zeolites, underscores their potential in high-throughput screening of MOFs for diverse applications.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Surviving Under Pressure: The Role of Solvent, Crystal Size, and Morphology During Pelletization of Metal–Organic Frameworks

As metal–organic frameworks (MOFs) gain traction for applications, such as hydrogen storage, it is essential to form the as-synthesized powder materials into shaped bodies with high packing densities to maximize their volumetric performance. Mechanical compaction, which involves compressing the materials at high pressure, has been reported to yield high monolith density but often results in a significant loss in accessible porosity. Herein, we sought to systematically control (1) crystal size, (2) solvation, and (3) compacting pressure in the pelletization process to achieve high packing density without compromising the porosity that makes MOFs functional. It was determined that solvation is the most critical factor among the three factors examined. Solvation that exceeds the pore volume prevents the framework from collapsing, allowing for porosity to be maintained through pelletization. Higher pelletization pressure results in higher packing density, with extensive loss of porosity being observed at a higher pressure if the solvation is below the pore volume. Lastly, we observed that the morphology and size of the MOF particles result in variation in the highest achievable packing efficiency, but these numbers (75%) are still greater than many existing techniques used to form MOFs. Furthermore, we concluded that the application of pressure through pelletization is a suitable and widely applicable technique for forming high-density MOF-monoliths.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

The IDAES process modeling framework and model library—Flexibility for process simulation and optimization

Abstract Energy systems and manufacturing processes of the 21st century are becoming increasingly dynamic and interconnected, which require new capabilities to effectively model and optimize their design and operations. Such next generation computational tools must leverage state‐of‐the‐art techniques in optimization and be able to rapidly incorporate new advances. To address these requirements, we have developed the Institute for the Design of Advanced Energy Systems (IDAES) Integrated Platform, which builds on the strengths of both process simulators (model libraries) and algebraic modeling languages (advanced solvers). This paper specifically presents the IDAES Core Modeling Framework (IDAES‐CMF), along with a case study demonstrating the application of the framework to solve process optimization problems. Capabilities provided by this framework include a flexible, modifiable, open‐source platform for optimization of process flowsheets utilizing state‐of‐the‐art solvers and solution techniques, fully open and extensible libraries of dynamic unit operations models and thermophysical property models, and integrated support for superstructure‐based conceptual design and optimization under uncertainty.

42 ENGINEERING↗

DOE OSTI2020

Stepwise Assembly of Turn‐on Fluorescence Sensors in Multicomponent Metal–Organic Frameworks for in Vitro Cyanide Detection

Abstract The controlled synthesis of multicomponent metal–organic frameworks (MOFs) allows for the precise placement of multiple cooperative functional groups within a framework, leading to emergent synergistic effects. Herein, we demonstrate that turn‐on fluorescence sensors can be assembled by combining a fluorophore and a recognition moiety within a complex cavity of a multicomponent MOF. An anthracene‐based fluorescent linker and a hemicyanine‐containing CN − ‐responsive linker were sequentially installed into the lattice of PCN‐700. The selective binding of CN − to hemicyanine inhibited the energy transfer between the two moieties, resulting in a fluorescence turn‐on effect. Taking advantage of the high tunability of the MOF platform, the ratio between anthracene and the hemicyanine moiety could be fine‐tuned in order to maximize the sensitivity of the overall framework. The optimized MOF‐sensor had a CN − ‐detection limit of 0.05 μ m , which is much lower than traditional CN − fluorescent sensors (about 0.2 μ m ).

Li, Jialuo↗

DOE OSTI2020

High‐Valent Metal–Oxo Species at the Nodes of Metal–Triazolate Frameworks: The Effects of Ligand Exchange and Two‐State Reactivity for C−H Bond Activation

Abstract Through quantum‐chemical calculations, we investigate a family of metal–organic frameworks (MOFs) containing triazolate linkers, M 2 X 2 (BBTA) (M=metal, X=bridging anion, H 2 BBTA=1 H ,5 H ‐benzo(1,2‐d:4,5‐d′)bistriazole), for their ability to form terminal metal–oxo sites and subsequently activate the C−H bond of methane. By varying the metal and bridging anion in the framework, we show how to significantly tune the reactivity of this series of MOFs. The electronic structure of the metal–oxo active site is analyzed for each combination of metal and bridging ligand, and we find that spin density localized on the oxo ligand is not an inherent requirement for low C−H activation barriers. For the Mn‐ and Fe‐containing frameworks, a transition from ferromagnetic to antiferromagnetic coupling between the metal binding site and terminal oxo ligand during the C−H activation process can greatly reduce the kinetic barrier, a unique case of two‐state reactivity without a change in the net spin multiplicity.

Rosen, Andrew S.↗

DOE OSTI2020

Isoreticular Linker Substitution in Conductive Metal–Organic Frameworks with Through‐Space Transport Pathways

Abstract The extension of reticular chemistry concepts to electrically conductive three‐dimensional metal–organic frameworks (MOFs) has been challenging, particularly for cases in which strong interactions between electroactive linkers create the charge transport pathways. Here, we report the successful replacement of tetrathiafulvalene (TTF) with a nickel glyoximate core in a family of isostructural conductive MOFs with Mn 2+ , Zn 2+ , and Cd 2+ . Different coordination environments of the framework metals lead to variations in the linker stacking geometries and optical properties. Single‐crystal conductivity data are consistent with charge transport along the linker stacking direction, with conductivity values only slightly lower than those reported for the analogous TTF materials. These results serve as a case study demonstrating how reticular chemistry design principles can be extended to conductive frameworks with significant intermolecular contacts.

Xie, Lilia S.↗

DOE OSTI2020

Directional Bonding in Decaniobate Inorganic Frameworks

Abstract Metal–oxo clusters offer an opportunity to assemble inorganic and metal–organic frameworks (MOFs) by a controlled building‐block approach, which led to the revolutionary discoveries of zeolites and MOFs. Polyoxometalate clusters are soluble in water, but more challenging to link into frameworks; the inert oxo‐caps that provide solubility are resistant to replacement or further connectivity. We demonstrate how the unique directional bonding and varying basicity of the decaniobate ([Nb 10 ]) oxo‐caps can be exploited to build 1D, 2D, and 3D inorganic frameworks. In nine structures, A + (A=Li, Na, K, Rb and Cs), AE 2+ (AE=Ca, Sr, Ba) and Mn 2+ demonstrate that the dimensionality of the obtained material is controlled by cation charge and size. Increased cation charge decreases selectivity for oxo‐site bonding, leading to higher dimensional linking. Larger cation radii also decreases bonding selectivity, yielding higher dimensional materials. Ion‐exchange studies of the A + ‐Nb 10 family shows exclusive selectivity for Cs + over other alkalis, which is important for radioactive Cs removal and sequestration.

Martin, Nicolas P.↗

DOE OSTI2020

High-Pressure Synthesis of Metal–Inorganic Frameworks Hf 4 N 20 ·N 2 , WN 8 ·N 2 , and Os 5 N 28 ·3 N 2 with Polymeric Nitrogen Linkers

Polynitrides are intrinsically thermodynamically unstable at ambient conditions and require peculiar synthetic approaches. Now, a one-step synthesis of metal–inorganic frameworks Hf 4 N 20 ·N 2 , WN 8 ·N 2 , and Os 5 N 28 ·3 N 2 via direct reactions between elements in a diamond anvil cell at pressures exceeding 100 GPa is reported. The porous frameworks (Hf 4 N 20 , WN 8 , and Os 5 N 28 ) are built from transition-metal atoms linked either by polymeric polydiazenediyl (polyacetylene-like) nitrogen chains or through dinitrogen units. Triply bound dinitrogen molecules occupy channels of these frameworks. Owing to conjugated polydiazenediyl chains, these compounds exhibit metallic properties. The high-pressure reaction between Hf and N 2 also leads to a non-centrosymmetric polynitride Hf 2 N 11 that features double-helix catena-poly[tetraz-1-ene-1,4-diyl] nitrogen chains [–N–N–N=N–] ∞ .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

A Quinoidal Two‐Dimensional Metal–Organic Framework for High‐Performance Micro‐Supercapacitors and Solid‐State Lithium Batteries

Two-dimensional (2D) metal-organic frameworks (MOFs) integrating redox-active linkers enable dense charge sites and open ion pathways for microscale energy storage. We report a quinoidal dicarboxylate ligand, AQM-H2L, derived from p-azaquinodimethane, forming crystalline frameworks with Cu2+ and Zn2+ nodes. The Cu-based MOF (AQM-AQM-H2L-Cu) exhibits layered sheets (>14 Å spacing) constructed from dinuclear Cu-carboxylate units and conjugated AQM linkers, which narrow the bandgap and introduce Cu2+/Cu+ pseudocapacitance. Exfoliated nanosheets (∼5 nm) retain crystallinity and excellent processability. Integrated into graphene films, they deliver areal and volumetric capacitances of 29.6 mF cm-2 and 18.1 F cm-3, achieving 2.6 mWh cm-3 energy density at 160 mW cm-3. As ionic fillers (1 wt%) in PEO solid polymer electrolytes, the nanosheets markedly enhance LiFePO4 cell performance, affording 169.8 mAh g-1 at 0.2 C and 93% retention after 400 cycles. This work establishes quinoidal linkers as a compact and robust design motif for ionically active 2D frameworks toward high-performance miniature and solid-state energy devices.

Chen, Ziman↗

DOE OSTI2023

Phosphate‐functionalized Zirconium Metal–Organic Frameworks for Enhancing Lithium–Sulfur Battery Cycling

Abstract Lithium–sulfur batteries are promising candidates for next‐generation energy storage devices due to their outstanding theoretical energy density. However, they suffer from low sulfur utilization and poor cyclability, greatly limiting their practical implementation. Herein, we adopted a phosphate‐functionalized zirconium metal–organic framework (Zr‐MOF) as a sulfur host. With their porous structure, remarkable electrochemical stability, and synthetic versatility, Zr‐MOFs present great potential in preventing soluble polysulfides from leaching. Phosphate groups were introduced to the framework post‐synthetically since they have shown a strong affinity towards lithium polysulfides and an ability to facilitate Li ion transport. The successful incorporation of phosphate in MOF‐808 was demonstrated by a series of techniques including infrared spectroscopy, solid‐state nuclear magnetic resonance spectroscopy, and X‐ray pair distribution function analysis. When employed in batteries, phosphate‐functionalized Zr‐MOF (MOF‐808‐PO4) exhibits significantly enhanced sulfur utilization and ion diffusion compared to the parent framework, leading to higher capacity and rate capability. The improved capacity retention and inhibited self‐discharge rate also demonstrate effective polysulfide encapsulation utilizing MOF‐808‐PO4. Furthermore, we explored their potential towards high‐density batteries by examining the cycling performance at various sulfur loadings. Our approach to correlate structure with function using hybrid inorganic–organic materials offers new chemical design strategies for advancing battery materials.

Liu, Bingqian↗

DOE OSTI2025

Decoding Antenna Behavior in Metal—Organic Frameworks

Metal–organic frameworks (MOFs) define a solid-state platform for developing artificial photosystems. Efficient anisotropic exciton migrations in these frameworks entail “antenna behavior” that can power up the distal interior reaction centers (RC), driving charge separation between donor-acceptor pairs. Reminiscent of the natural light-harvesting complex, such processes can achieve high quantum yield by exploiting the vast interior surface of the porous crystallites. It is important to understand the optimum positioning of the RC site relative to the anisotropic exciton migration path within these frameworks. The efficiency of such antenna behavior is probed here through Stern–Volmer (SV) type analysis with a series of node-anchored redox quenchers, ferrocene-carboxylate, ferrocene acetate, and dinitrobenzoate. Decoding various intrinsic processes, this work constructs a revised SV formalism in solid assembly that hosts ultrafast anisotropic exciton migration to account for the intrinsic exciton hopping rate from the extrinsic electron transfer rate, and the dimension of effective quenching. In conclusion, this transformative understanding can be applied to other relevant solid-state assemblies.

Saha, Bapan [Southern Illinois University, Carbond↗

DOE OSTI2023

Jay : A software framework for prototyping and evaluating offloading applications in hybrid edge clouds

Abstract We present Jay , a software framework for offloading applications in hybrid edge clouds. Jay provides an API, services, and tools that enable mobile application developers to implement, instrument, and evaluate offloading applications using configurable cloud topologies, offloading strategies, and job types. We start by presenting Jay 's job model and the concrete architecture of the framework. We then present the programming API with several examples of customization. Then, we turn to the description of the internal implementation of Jay instances and their components. Finally, we describe the Jay Workbench, a tool that allows the setup, execution, and reproduction of experiments with networks of hosts with different resource capabilities organized with specific topologies. The complete source code for the framework and workbench is provided in a GitHub repository.

Silva, Joaquim↗

DOE OSTI2020

Framework for assessing land‐management effects on at‐risk species: Example of SE USA wood pellet production and gopher tortoise ( Gopherus polyphemus )

Abstract Land‐management choices made for economic and societal gains intrinsically influence landscapes and species that are dependent upon them. We propose a simple analysis framework to examine critical intersections between land‐management choices and the life‐history conditions of selected species of concern, thereby facilitating the identification of mitigation practices that can reduce negative impacts on species at risk. We test the proposed framework through application to gopher tortoise ( Gopherus polyphemus ), a keystone species that is the focus of conservation efforts across the southeastern region of United States of America, where wood pellets are being produced for bioenergy. Production of these wood pellets for export to Europe and Asia has drastically increased in the past decade, raising concerns about potential harm to biodiversity since many species in the forests sourcing pellet production were already at risk prior to the development of this new commodity. Identifying the mechanisms of potential impacts of wood pellet production on species of concern is essential to establishing meaningful management recommendations that can enhance conservation efforts while supporting sustainable bioenergy. By considering the intersections between life‐history conditions of gopher tortoise and forest‐management practices related to woody biomass extraction for pellet production, we identify several mechanisms by which the wood‐pellet industry might affect this species of concern, both positively and negatively. We then identify mitigation practices that can help offset the potential impacts of logging, thinning, and dead wood removal on gopher tortoise. Our analysis framework may be transferable to other species of concern and land‐management practices across diverse landscapes. This article is categorized under: Bioenergy > Economics and Policy Bioenergy > Climate and Environment

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Exploring Saccharomycotina Yeast Ecology Through an Ecological Ontology Framework

Yeasts in the subphylum Saccharomycotina are found across the globe in disparate ecosystems. A major aim of yeast research is to understand the diversity and evolution of ecological traits, such as carbon metabolic breadth, insect association, and cactophily. This includes studying aspects of ecological traits like genetic architecture or association with other phenotypic traits. Genomic resources in the Saccharomycotina have grown rapidly. Ecological data, however, are still limited for many species, especially those only known from species descriptions where usually only a limited number of strains are studied. Moreover, ecological information is recorded in natural language format limiting high throughput computational analysis. To address these limitations, we developed an ontological framework for the analysis of yeast ecology. A total of 1,088 yeast strains were added to the Ontology of Yeast Environments (OYE) and analyzed in a machine-learning framework to connect genotype to ecology. This framework is flexible and can be extended to additional isolates, species, or environmental sequencing data. Widespread adoption of OYE would greatly aid the study of macroecology in the Saccharomycotina subphylum.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Developing a Consistent Travel-Time Framework for Comparing Three-Dimensional Velocity Models for Seismic Location Accuracy

Abstract Location algorithms have historically relied on simple, one-dimensional (1D) velocity models for fast seismic event locations. 1D models are generally used as travel-time lookup tables, one for each seismic phase, with travel-times pre-calculated for event distance and depth. These travel-time lookup tables are extremely fast to use and this fast computational speed makes them the preferred type of velocity model for operational needs. Higher-dimensional (i.e., three-dimensional—3D) seismic velocity models are becoming readily available and provide more accurate event locations over 1D models. The computational requirements of these 3D models tend to make their operational use prohibitive. Additionally, comparing location accuracy for 3D seismic velocity models tends to be problematic, as each model is determined using different ray-tracing algorithms. Attempting to use a different algorithm than the one used to develop a model usually results in poor travel-time prediction. We demonstrate and test a framework to create first-P and first-S 3D travel-time correction surfaces using an open-source framework ( PCalc + GeoTess , https://www.sandia.gov/salsa3d/software/geotess ) that easily stores 3D travel-time and uncertainty data. This framework produces fast travel-time and uncertainty predictions and overcomes the ray-tracing algorithm hurdle because the lookup tables can be generated using the exact ray-tracing algorithm that is preferred for a model.

3D velocity models↗

DOE OSTI2022

A framework for detection and attribution of regional precipitation change: Application to the United States historical record

Abstract Despite the emerging influence of anthropogenic climate change on the global water cycle, at regional scales the combination of observational uncertainty, large internal variability, and modeling uncertainty undermine robust statements regarding the human influence on precipitation. Here, we use output from global climate models in a perfect-data sense to develop a framework for conducting regional detection and attribution (D&A) for precipitation, starting with the contiguous United States (CONUS) where observational uncertainty is lower than in other regions. Our unified approach can simultaneously detect systematic trends in mean and extreme precipitation, attribute trends to anthropogenic forcings, compute the effects of forcings as a function of time, and map the effects of individual forcings. Model output is used to conduct a set of tests that yield a parsimonious representation for characterizing seasonal precipitation over the CONUS for the historical record (1900 to present day), which ensures our D&A is insensitive to structural uncertainty. Our framework is developed using synthetic data in a Pearl-causal perspective wherein causality can be identified using intervention-based simulations. While the hypothesis-based framework and accompanying generalized D&A formula we develop should be widely applicable, we include a strong caution that the hypothesis-guided simplification of the formula for the historical climatic record of CONUS as described in this paper will likely fail to hold in other geographic regions and under future warming.

54 ENVIRONMENTAL SCIENCES↗