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DOE OSTI2026

Group-Additivity–Embedded Multiscale Modeling for Electric Field-Enhanced Nanocatalysis

Elucidating structure-performance relationships remains a central challenge in field-enhanced catalysis, where nanoparticles exhibit nonuniform surface sites with site-dependent responses to electric fields. Low-coordination sites (edges, corners, and tips) are particularly electric field-sensitive (EF), leading to nonuniform charge distribution, adsorption energies, and catalytic activity. Here, using ammonia decomposition on a ruthenium cluster as a model system, we develop a transferable multiscale framework integrating density functional theory, group additivity (GA), Brønsted-Evans-Polanyi scaling, and microkinetic modeling to predict EF-dependent activity across nonuniform cluster sites. Across sites and fields, the nitrogen adsorption energy (E N ) emerges as the governing descriptor, yielding robust volcano relationships whose optimum shifts systematically with field: negative fields strengthen N binding via electron accumulation, while positive fields weaken N binding via charge depletion, moving the optimal E N toward weaker binding. Microkinetic analysis shows that N≡N bond formation remains the key kinetic bottleneck over most conditions; positive fields lower the effective barrier and, critically, increase the fraction of near-optimal active sites, leading to a net enhancement in overall activity relative to zero-field and negative-field cases. By capturing EF- and site-dependent energetics with high accuracy and low computational cost, this GA-embedded multi-scale simulation workflow provides a physically interpretable route to predict and design field-enhanced nanocatalysis.

ammonia decomposition↗

DOE OSTI2020

Data-driven variational multiscale reduced order models

We propose a new data-driven reduced order model (ROM) framework that centers around the hierarchical structure of the variational multiscale (VMS) methodology and utilizes data to increase the ROM accuracy at a modest computational cost. The VMS methodology is a natural fit for the hierarchical structure of the ROM basis: In the first step, we use the ROM projection to separate the scales into three categories: (i) resolved large scales, (ii) resolved small scales, and (iii) unresolved scales. In the second step, we explicitly identify the VMS–ROM closure terms, i.e., the terms representing the interactions among the three types of scales. In the third step, we use available data to model the VMS–ROM closure terms. Thus, instead of phenomenological models used in VMS for standard numerical discretizations (e.g., eddy viscosity models), we utilize available data to construct new structural VMS–ROM closure models. Specifically, we build ROM operators (vectors, matrices, and tensors) that are closest to the true ROM closure terms evaluated with the available data. We test the new data-driven VMS–ROM in the numerical simulation of four test cases: (i) the 1D Burgers equation with viscosity coefficient v = 10 -3 ; (ii) a 2D flow past a circular cylinder at Reynolds numbers, Re = 100, Re = 500, and Re= 1000; (iii) the quasi-geostrophic equations at Reynolds number Re = 450 and Rossby number Ro = 0.0036; and (iv) a 2D flow over a backward facing step at Reynolds number Re = 1000. The numerical results show that the data-driven VMS–ROM is significantly more accurate than standard ROMs.

42 ENGINEERING↗

DOE OSTI2020

Solvent selection for biphasic extraction of 5-hydroxymethylfurfural via multiscale modeling and experiments

We introduce a comprehensive conceptual framework for selecting solvents for reactive extraction in biphasic organic-water systems and demonstrate it for the separation of HMF (5-hydroxylmethylfurfural), a platform chemical produced in the acid-catalyzed dehydration of hexoses. We first perform in silico screening of ~2500 solvents, from the ADFCRS-2018 database using the ADF COSMO-RS implementation, and classification, based on the solvent partition coefficient. We then determine experimentally the partition coefficients for HMF, fructose, and products of HMF rehydration (levulinic acid (LA), and formic acid (FA)), the mutual water-organic solvent solubilities, and the separation factors in >50 select solvents spanning multiple homologous series at room temperature and a typical reaction temperature with in situ sampling. We find that COSMO-RS is excellent for screening purposes (typical error in most cases within a factor of ~2). Increased temperatures lead to significant reduction in partitioning, and room temperature measurements are clearly inadequate for solvent selection. Upon down selecting classes of solvents based on separation performance, we perform experimental thermal stability and reaction compatibility studies of a small set of solvents at relevant reactive-extraction temperatures. We discover that many substituted phenols exhibit an order-of-magnitude increase in partitioning compared to conventional solvents due chiefly to hydrogen bond interactions and show the necessary stability but retain a significant fraction of water and LA, factors that need to be considered in technoeconomic analysis. In contrast, anilines, aldehydes, and acids are good to excellent regarding separation but incompatible with this specific reaction medium. Furthermore, this multifaceted framework can be extended to other biomass-derived products and processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Multiscale modeling of HBV infection integrating intra- and intercellular viral propagation to analyze extracellular viral markers

Chronic infection with hepatitis B virus (HBV) is caused by the persistence of closed circular DNA (cccDNA) in the nucleus of infected hepatocytes. Despite available therapeutic anti-HBV agents, eliminating the cccDNA remains challenging. Thus, quantifying and understanding the dynamics of cccDNA are essential for developing effective treatment strategies and new drugs. However, such study requires repeated liver biopsy to measure the intrahepatic cccDNA, which is basically not accepted because liver biopsy is potentially morbid and not common during hepatitis B treatment. We here aimed to develop a noninvasive method for quantifying cccDNA in the liver using surrogate markers in peripheral blood. We constructed a multiscale mathematical model that explicitly incorporates both intracellular and intercellular HBV infection processes. The model, based on age-structured partial differential equations, integrates experimental data from in vitro and in vivo investigations. By applying this model, we roughly predicted the amount and dynamics of intrahepatic cccDNA within a certain range using specific viral markers in serum samples, including HBV DNA, HBsAg, HBeAg, and HBcrAg. Our study represents a significant step towards advancing the understanding of chronic HBV infection. The noninvasive quantification of cccDNA using our proposed method holds promise for improving clinical analyses and treatment strategies. By comprehensively describing the interactions of all components involved in HBV infection, our multiscale mathematical model provides a valuable framework for further research and the development of targeted interventions.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Accelerating Finite-temperature Kohn-Sham Density Functional Theory\ with Deep Neural Networks

We present a numerical modeling workflow based on machine learning (ML) which reproduces the the total energies produced by Kohn-Sham density functional theory (DFT) at finite electronic temperature to within chemical accuracy at negligible computational cost. Based on deep neural networks, our workflow yields the local density of states (LDOS) for a given atomic configuration. From the LDOS, spatially-resolved, energy-resolved, and integrated quantities can be calculated, including the DFT total free energy, which serves as the Born-Oppenheimer potential energy surface for the atoms. We demonstrate the efficacy of this approach for both solid and liquid metals and compare results between independent and unified machine-learning models for solid and liquid aluminum. Our machine-learning density functional theory framework opens up the path towards multiscale materials modeling for matter under ambient and extreme conditions at a computational scale and cost that is unattainable with current algorithms.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Accelerating Finite-Temperature Kohn-Sham Density Functional Theory with Deep Neural Networks

We present a numerical modeling workflow based on machine learning (ML) which reproduces the total energies produced by Kohn-Sham density functional theory (DFT) at finite electronic temperature to within chemical accuracy at negligible computational cost. Based on deep neural networks, our workflow yields the local density of states (LDOS) for a given atomic configuration. From the LDOS, spatially-resolved, energy-resolved, and integrated quantities can be calculated, including the DFT total free energy, which serves as the Born-Oppenheimer potential energy surface for the atoms. We demonstrate the efficacy of this approach for both solid and liquid metals and compare results between independent and unified machine-learning models for solid and liquid aluminum. Our machine-learning density functional theory framework opens up the path towards multiscale materials modeling for matter under ambient and extreme conditions at a computational scale and cost that is unattainable with current algorithms.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Modeling the 4D discharge of lithium-ion batteries with a multiscale time-dependent deep learning framework

The lithium-ion battery (LIB) field is moving towards the direction of investigating spatially resolved physical phenomena in the 3D porous microstructure of electrodes. These pore-scale simulations give new insights into the local dynamics of lithiation/de-lithiation and charge transport, Nevertheless, the computational time of these simulations limits the integration of these models in optimization workflows of cycling conditions or electrode manufacturing processes. Machine learning models present a way of assessing in real-time the performance of materials. While several successful techniques for replicating simulations with machine learning have been proposed, this case study presents a more demanding problem, due to the necessity of understanding the behavior of heterogeneous 3D local data, as it evolves in time: this poses both a scientific and a technical challenge. To this end, we propose an autoregressive multiscale convolutional neural network model to predict relevant quantities at the pore-scale in the solid phase: the lithium concentration (in the active material) and potential (in the active material and carbon binder). Here, these are ultimately used to reconstruct the battery discharge curve. 3D images of the electrode microstructures are the input to the network, trained with a dataset of finite element method simulations to predict the discharge behavior of the cathode side in lithium ion batteries. We propose this machine learning model as a proof-of-concept of the applicability of multiscale networks for time-dependent physics problems. The trained model exhibits very high accuracy (with errors lower than 2 %) in forecasting the discharge behavior of new unseen cathodes.

25 ENERGY STORAGE↗

DOE OSTI2021

Generalizable coordination of large multiscale workflows: challenges and learnings at scale

The advancement of machine learning techniques and the heterogeneous architectures of most current supercomputers are propelling the demand for large multiscale simulations that can automatically and autonomously couple diverse components and map them to relevant resources to solve complex problems at multiple scales. Nevertheless, despite the recent progress in workflow technologies, current capabilities are limited to coupling two scales. In the first-ever demonstration of using three scales of resolution, we present a scalable and generalizable framework that couples pairs of models using machine learning and in situ feedback. We expand upon the massively parallel Multiscale Machine-Learned Modeling Infrastructure (MuMMI), a recent, award-winning workflow, and generalize the framework beyond its original design. We discuss the challenges and learnings in executing a massive multiscale simulation campaign that utilized over 600,000 node hours on Summit and achieved more than 98% GPU occupancy for more than 83% of the time. We present innovations to enable several orders of magnitude scaling, including simultaneously coordinating 24,000 jobs, and managing several TBs of new data per day and over a billion files in total. Finally, we describe the generalizability of our framework and, with an upcoming open-source release, discuss how the presented framework may be used for new applications.

Bhatia, Harsh↗

DOE OSTI2021

Data assimilation empowered neural network parametrizations for subgrid processes in geophysical flows

In the past couple of years, there has been a proliferation in the use of machine learning approaches to represent subgrid-scale processes in geophysical flows with an aim to improve the forecasting capability and to accelerate numerical simulations of these flows. Despite its success for different types of flow, the online deployment of a data-driven closure model can cause instabilities and biases in modeling the overall effect of subgrid-scale processes, which in turn leads to inaccurate prediction. To tackle this issue, we exploit the data assimilation technique to correct the physics-based model coupled with the neural network as a surrogate for unresolved flow dynamics in multiscale systems. In particular, we use a set of neural network architectures to learn the correlation between resolved flow variables and the parametrizations of unresolved flow dynamics and formulate a data assimilation approach to correct the hybrid model during their online deployment. We illustrate our framework in a set of applications of the multiscale Lorenz 96 system for which the parametrization model for unresolved scales is exactly known, and the two-dimensional Kraichnan turbulence system for which the parametrization model for unresolved scales is not known a priori. Our analysis, therefore, comprises a predictive dynamical core empowered by (i) a data-driven closure model for subgrid-scale processes, (ii) a data assimilation approach for forecast error correction, and (iii) both data-driven closure and data assimilation procedures. We show significant improvement in the long-term prediction of the underlying chaotic dynamics with our framework compared to using only neural network parametrizations for future prediction. Moreover, we demonstrate that these data-driven parametrization models can handle the non-Gaussian statistics of subgrid-scale processes, and effectively improve the accuracy of outer data assimilation workflow loops in a modular nonintrusive way.

42 ENGINEERING↗

DOE OSTI2020

Advances in Multiscale Modeling of Lignocellulosic Biomass

Applications and associated processing technologies of lignocellulosic biomass are becoming more important as we endeavor to meet societal demand for fuels, chemicals, and materials from renewable resources. Meanwhile, the rapidly expanding availability and capabilities of high-performance computing present an unprecedented opportunity to accelerate development of technologies surrounding lignocellulose utilization. In order to realize this potential, suitable modeling frameworks must be constructed that effectively capture the multiscale complexity and tremendous variety exhibited by lignocellulosic materials. In our assessment of previous endeavors towards this goal, several important shortcomings have been identified: (1) the lack of multiscale integration strategies that capture emergent properties and behaviors spanning different length scales, and (2) the inability of many modeling approaches to effectively capture the variability and diversity of lignocellulose that arise from both natural and process-induced sources. In this perspective, we survey previous modeling approaches for lignocellulose and simulations processes involving its chemical and mechanical transformation, and suggest opportunities for future development to enhance the utility of computational tools to address barriers to widespread adoption of a renewable bioeconomy.

09 BIOMASS FUELS↗

DOE OSTI2023

A deep learning upscaling framework: Reactive transport and mineral precipitation in fracture-matrix systems

Pore-scale modeling has limited applicability at large scales due to its high computational cost. One common approach to upscale pore-scale models is the use of effective medium theories, which homogenize small-scale features in a porous structure and characterize the medium by macroscale properties (e.g., permeability) and equations (e.g., Darcy’s law). However, there are classes of physical processes for which effective medium approximations may become inaccurate, e.g., mineral precipitation and clogging during reactive transport. We have developed a deep learning upscaling framework, in which pore-scale modeling is directly employed in macroscale systems, without relying on effective medium approximations. The upscaling framework is first developed for general multiscale systems and then applied to modeling reactive transport with mineral precipitation in the altered layer in fracture-matrix structures. Solute transport from the fractures to the matrix is modeled as a wall boundary condition for the fractures, which, in turn, is predicted by recurrent neural networks using the concentration histories at the fracture-matrix boundary. Specifically, we consider a meter-scale fracture network embedded in sandstones, where the smallest feature is at the micron scale. Here the proposed framework allows us to span five orders of magnitude in length scales by capturing mineral precipitation in the altered layer of the rock matrix at the pore scale across the entire meter-scale fracture network.

42 ENGINEERING↗

DOE OSTI2022

Multiscale simulation of integrated energy system and electricity market interactions

Accelerating the deep decarbonization of the world's electric grids requires the coordination of complex energy systems and infrastructures across timescales from seconds to decades. Here, we present a new multiscale simulation framework that integrates process- and grid-centric modeling paradigms to better design, operate, and control integrated energy systems (IESs), which combine multiple technologies, in wholesale energy markets. Traditionally, IESs are analyzed with a process-centric paradigm such as levelized cost of electricity (LCOE) or annualized net revenue, ignoring important interactions with electricity markets. This framework explicitly models the complex interactions between an IES's bidding, scheduling, and control decisions and the energy market's clearing and settlement processes, while incorporating operational uncertainties. Through two case studies, we show the importance of understanding and quantifying complex resource-grid interactions. In case study 1, we demonstrate that optimized bidding from one resource shifts the profit distribution for all energy systems in the market. This result suggests new and more flexible IES technologies can disrupt the economics of all market participants, possibly leading to accelerated retirements of less flexible resources. Interestingly, the optimized bidding has little impact on grid-level aggregate statistics, such as total generation costs and renewable penetration rate. While aggregate modeling strategies may remain valid under some IES adoption scenarios for analysis focused on regional outcomes, direct comparisons of IES technologies at specific locations without considering these interactions may lead to misleading or incorrect conclusions. In case study 2, we consider the design and flexible operation of IESs that hybridize conventional generators with energy storage. Through a sensitivity analysis, we find that as the size of the storage system increases, the total number of start-ups for coal- and natural gas-based IESs reduced by 25% and 33.6%, and the total thermal generator ramping (i.e., mileage) reduced by 86.5% and 62.5%, respectively. This shows the primary benefit of storage may not be reduced operational costs (which do not change significantly) but fewer start-ups and less ramping, which may greatly simplify the design, operation, and control of carbon capture systems. The new modeling and optimization capabilities from this work enable the coupling of rigorous, dynamic process models with grid-level production cost models to quantitatively identify the nuanced interdependencies across these vast timescales that must be addressed to realize clean, safe, and secure energy production. Moreover, the proposed general multiscale simulation framework is applicable to all IES technologies and can be easily extended to consider other energy carriers (e.g., hydrogen, ammonia) and energy infrastructures (e.g., natural gas pipelines).

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2026

Integrated Computational Materials Engineering (ICME) Capability Maturity Levels for Ecosystems Enabling Digital Transformation

Digital engineering (DE) and integrated computational materials engineering (ICME) are widely recognized as critical enablers of faster, more affordable, and more reliable aerospace systems. However, many organizations have struggled to realize the promised return on investment (ROI) from digital initiatives. A primary reason is the absence of a shared, decision-focused framework that distinguishes simple digitization of existing workflows from true digital transformation that fundamentally changes how engineering decisions are made. This paper introduces an ICME capability maturity framework that fills this gap. The framework defines six cumulative ICME capability maturity levels (CMLs), explicitly tied to decision authority, engineering integration, optimization, and uncertainty management across material, process, structure, and performance scales. It is designed to complement established readiness metrics such as technology readiness levels (TRLs), manufacturing readiness levels (MRLs), and integration readiness levels (IRLs), by addressing a missing dimension: the conditions required for model-informed decision authority across scales. A unifying figure and capability table illustrate the six-level ICME Capability Maturity Framework, showing how organizations progress from digitization—with limited or negative ROI—to true digital transformation, where ICME-enabled workflows deliver measurable improvements in decision quality, cycle time, risk reduction, and reuse. The framework is intended for both technical practitioners and executive leadership, providing a common language to assess current state, guide roadmaps, align software ecosystem investments, and set realistic expectations for digital transformation outcomes. A regulatory-relevant statement clarifying the relationship between ICME capability and existing certification frameworks is provided.

ICME↗

DOE OSTI2024

Task-oriented machine learning surrogates for tipping points of agent-based models

We present a machine learning framework bridging manifold learning, neural networks, Gaussian processes, and Equation-Free multiscale approach, for the construction of different types of effective reduced order models from detailed agent-based simulators and the systematic multiscale numerical analysis of their emergent dynamics. The specific tasks of interest here include the detection of tipping points, and the uncertainty quantification of rare events near them. Our illustrative examples are an event-driven, stochastic financial market model describing the mimetic behavior of traders, and a compartmental stochastic epidemic model on an Erdös-Rényi network. We contrast the pros and cons of the different types of surrogate models and the effort involved in learning them. Importantly, the proposed framework reveals that, around the tipping points, the emergent dynamics of both benchmark examples can be effectively described by a one-dimensional stochastic differential equation, thus revealing the intrinsic dimensionality of the normal form of the specific type of the tipping point. This allows a significant reduction in the computational cost of the tasks of interest.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2026

Multiscale analysis of large twist ferroelectricity and swirling dislocations in bilayer hexagonal boron nitride

With its atomically thin structure and intrinsic ferroelectric properties, heterodeformed bilayer hexagonal boron nitride (hBN) has gained prominence in next-generation non-volatile memory applications. However, studies to date have focused almost exclusively on small-twist bilayer hBN, leaving the question of whether ferroelectricity can persist under small heterostrain and large heterodeformation entirely unexplored. In this work, we establish the crystallographic origin of ferroelectricity in bilayer hBN configurations heterodeformed relative to high-symmetry configurations such as AA-stacking and 21.786789° twisted configurations (Σ7), using Smith normal form bicrystallography. We then demonstrate out-of-plane ferroelectricity in bilayer hBN across configurations vicinal to both the AA and Σ7 stackings. Atomistic simulations reveal that AA-vicinal systems support ferroelectricity under both small twist and small strain, with polarization switching in the latter governed by the deformation of swirling dislocations rather than the straight interface dislocations seen in the former. For Σ7-vicinal systems, where existing interatomic potentials underperform particularly under extreme out-of-plane compression, we develop a density-functional-theory-informed continuum framework—the bicrystallography-informed frame-invariant multiscale (BFIM) model, which captures out-of-plane ferroelectricity in heterodeformed configurations vicinal to Σ7 stacking. Interface dislocations in these large heterodeformed bilayer configurations exhibit markedly smaller Burgers vectors compared to interface dislocations in small-twist and small-strain bilayer hBN. The BFIM model reproduces experimental results and provides a powerful, computationally efficient framework for predicting ferroelectricity in large-unit-cell heterostructures where atomistic simulations are prohibitively expensive.

Ahmed, Md Tusher [Univ. of Illinois at Urbana-Cham↗

DOE OSTI2022

HI-STORM Overpack and MPC-32 Thermal-Hydraulic Model with MOOSE Framework

Nuclear power is a significant source of electricity in the United States, but the average age of nuclear power plants is around 40 years old. The safe management of the spent nuclear fuel (SNF) is a key aspect of the back-end of the nuclear fuel cycle. Spent fuel dry storage systems are becoming a popular and effective solution in this area, given the absence of a final disposal system. The spent fuel cask system (dry cask method) provides a feasible solution to maintain spent fuel for 60 years before final disposal. Dry cask storage has many characteristics that make it attractive. It fulfills the safety requirements of the Nuclear Regulatory Commission (NRC) while providing modularity and flexibility to contractors. The HI-STORM overpack and MPC-32 canister are the main parts of the HI-STORM 100 dry cask storage system. These components remove heat from the system using natural circulation, requiring no human intervention. This is the characteristic that provides passive heat removal and low maintenance features in dry cask storage systems. To develop a thermal model for a dry cask storage system, the physics behind the system should be defined clearly. There are two natural circulation loops in the system; circulation of helium cools down the nuclear assemblies in the MPC, while circulation of air cools down the walls of the MPC. This work aims to develop a thermal model of the MPC-32 canister and HI-STORM overpack using the Multiphysics Object-Oriented Simulation Environment (MOOSE). MOOSE is an open-source framework developed by Idaho National Laboratory (INL) for multiscale, multiphysics simulations. In this study, we will investigate and demonstrate the thermal-hydraulics modeling capabilities of the MOOSE framework, including natural circulation, heat transfer, and porous flow.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Development of a MOOSE thermal model of the MPC-32 canister and HI-STORM overpack

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2022

Tritium Transport Modeling at the Pore Scale in Ceramic Breeder Materials Using TMAP8

Fusion reactors depend on the blanket material to breed and release tritium at the same rate or faster than it is consumed by the fusion reaction. Cellular ceramic breeders (CCBs) are dense materials that can maintain a high tritium breeding ratio while promoting tritium release because of highly connected pores. Assessing the tritium breeding capabilities of these materials requires a combination of extensive experimental and modeling efforts. In this work, we develop and calibrate a multiphysics model of tritium transport. This novel model accounts for ceramic and pore diffusion, trapping and detrapping, and several surface reactions at the pore surface. We perform a sensitivity analysis and calibrate the model by comparing its predictions against experimental measurements of deuterium absorption. The calibrated model is then used to model tritium absorption in samples with different pore microstructures to investigate the effect of pore interconnectivity on tritium absorption. The model is part of the development of the multiscale, multiphysics framework for tritium transport [i.e., the Tritium Migration Analysis Program (TMAP8)], which is itself built on top of the finite-element multiphysics framework multiphysics object-oriented simulation environment (MOOSE). This study demonstrates some of TMAP8’s capabilities and is the first step toward assessing the tritium breeding capabilities of ceramic breeder material designs.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗