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DOE OSTI2023

On the importance of three-dimensional modeling for high-energy-density physics experiments

Laser-driven cylindrical implosion experiments enable direct measurements of hydrodynamic instability growth in convergent geometries, providing a wealth of validation data in the high-energy-density regime. These experiments are designed to be nearly axially invariant, allowing for modeling with complementary two-dimensional slices of the cylinder. Two distinct hydrodynamics codes are employed to model a subset of these experiments, and the results are shown to be in very good agreement with each other and the available experimental data. While this 2D modeling approach adequately captures most of the physics of the implosion and ensuing instability growth, there are crucial aspects from the three-dimensional nature of the experiments that are missed in 2D. The first fully 3D simulations of these experiments are presented, and small but significant differences are found to arise from both the axial and azimuthal non-uniformity in the laser drive. Recent experimental results confirming the drive asymmetry are discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2021

Survey on stochastic distribution systems: A full probability density function control theory with potential applications

Complex systems seen either in general engineering practice or economics are subjected to ever increased uncertainties that are mostly represented as random variables or parameters, and the characteristics of random variables are represented by their probability density functions (PDFs). Controlling their PDFs means to shape their stochastic distributions and in general it would provide a full treatment for system analysis and operational control and optimization. This leads to the development of stochastic distribution control (SDC) systems theory in the past decades, where the original aim of the controller design is to realize a shape control of the distributions of certain random variables in their PDFs sense for some engineering processes. Indeed, once the PDFs of these random variables or parameters are used to describe their distribution characters, the control task is to obtain control signals so that the output PDFs of stochastic systems are made to follow their target PDFs. The subject of SDC was initially originated for non-Gaussian stochastic control systems design but has found a wide spectrum of applications in general systems in terms of data-driven modeling, analysis, signal processing (filtering), data mining via multivariable statistics, decision-making (optimization) for systems subjected to uncertainties and even in economics. In this context, SDC constitutes an effective primer tool for complex system analysis, control and operational optimizations. In this review paper, a detailed survey of the developments on the research of SDC systems will be made together with their wide spectrum applications and future perspectives.

42 ENGINEERING↗

DOE OSTI2022

Optimization of Thermal Conductance at Interfaces Using Machine Learning Algorithms

We report optimization of thermal transport across the interface of two different materials is critical to micro-/nanoscale electronic, photonic, and phononic devices. Although several examples of compositional intermixing at the interfaces having a positive effect on interfacial thermal conductance (ITC) have been reported, an optimum arrangement has not yet been determined because of the large number of potential atomic configurations and the significant computational cost of evaluation. On the other hand, computation-driven materials design efforts are rising in popularity and importance. Yet, the scalability and transferability of machine learning models remain as challenges in creating a complete pipeline for the simulation and analysis of large molecular systems. In this work we present a scalable Bayesian optimization framework, which leverages dynamic spawning of jobs through the Message Passing Interface (MPI) to run multiple parallel molecular dynamics simulations within a parent MPI job to optimize heat transfer at the silicon and aluminum (Si/Al) interface. We found a maximum of 50% increase in the ITC when introducing a two-layer intermixed region that consists of a higher percentage of Si. Because of the random nature of the intermixing, the magnitude of increase in the ITC varies. We observed that both homogeneity/heterogeneity of the intermixing and the intrinsic stochastic nature of molecular dynamics simulations account for the variance in ITC.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Sinuosity‐Driven Hyporheic Exchange: Hydrodynamics and Biogeochemical Potentials

Abstract Hydrologic exchange processes are critical for ecosystem services along river corridors. Meandering contributes to this exchange by driving channel water, solutes, and energy through the surrounding alluvium, a process called sinuosity‐driven hyporheic exchange. This exchange is embedded within and modulated by the regional groundwater flow (RGF), which compresses the hyporheic zone and potentially diminishes its overall impact. Quantifying the role of sinuosity‐driven hyporheic exchange at the reach‐to‐watershed scale requires a mechanistic understanding of the interplay between drivers (meander planform) and modulators (RGF) and its implications for biogeochemical transformations. Here, we use a 2D, vertically integrated numerical model for flow, transport, and reaction to analyze sinuosity‐driven hyporheic exchange systematically. Using this model, we propose a dimensionless framework to explore the role of meander planform and RGF in hydrodynamics and how they constrain nitrogen cycling. Our results highlight the importance of meander topology for water flow and age. We demonstrate how the meander neck induces a shielding effect that protects the hyporheic zone against RGF, imposing a physical constraint on biogeochemical transformations. Furthermore, we explore the conditions when a meander acts as a net nitrogen source or sink. This transition in the net biogeochemical potential is described by a handful of dimensionless physical and biogeochemical parameters that can be measured or constrained from literature and remote sensing. This work provides a new physically based model that quantifies sinuosity‐driven hyporheic exchange and biogeochemical reactions, a critical step toward their representation in water quality models and the design and assessment of river restoration strategies.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2021

On the study of hydrodynamic instabilities in the presence of background magnetic fields in high-energy-density plasmas

Blast-wave-driven hydrodynamic instabilities are studied in the presence of a background B-field through experiments and simulations in the high-energy-density (HED) physics regime. In experiments conducted at the Laboratoire pour l’utilisation des lasers intenses (LULI), a laser-driven shock-tube platform was used to generate a hydrodynamically unstable interface with a prescribed sinusoidal surface perturbation, and short-pulse x-ray radiography was used to characterize the instability growth with and without a 10-T B-field. The LULI experiments were modeled in FLASH using resistive and ideal magnetohydrodynamics (MHD), and comparing the experiments and simulations suggests that the Spitzer model implemented in FLASH is necessary and sufficient for modeling these planar systems. These results suggest insufficient amplification of the seed B-field, due to resistive diffusion, to alter the hydrodynamic behavior. Although the ideal-MHD simulations did not represent the experiments accurately, they suggest that similar HED systems with dynamic plasma-β (=2μ0ρv2/B2) values of less than ∼100 can reduce the growth of blast-wave-driven Rayleigh–Taylor instabilities. These findings validate the resistive-MHD FLASH modeling that is being used to design future experiments for studying B-field effects in HED plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2024

Validation of implosion modeling through direct-drive shock timing experiments at the National Ignition Facility

Precise modeling of shocks in inertial confinement fusion implosions is critical for obtaining the desired compression in experiments. Shock velocities and post-shock conditions are determined by laser-energy deposition, heat conduction, and equations of state. This paper describes experiments at the National Ignition Facility (NIF) where multiple shocks are launched into a cone-in-shell target made of polystyrene, using laser pulse shapes with two or three pickets and varying on-target intensities. Shocks are diagnosed using the Velocity Interferometric System for Any Reflector diagnostic. Simulated and inferred shock velocities agree well for the range of intensities studied in this work. These results are the first shock-velocity measurements using direct drive on the NIF and provide a good measure of early-time, directly driven laser-energy coupling. Furthermore, the validated models add to the credibility of direct-drive-ignition designs at the megajoule scale.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2026

EvoDiffMol: evolutionary diffusion framework for 3D molecular design with optimized properties

Designing molecules with specific target properties remains a fundamental challenge in computational chemistry. While existing approaches show promise, most rely on simplified representations like SMILES strings or 2D graphs that lack essential three-dimensional geometric information. We present EvoDiffMol, a computational framework that integrates evolutionary algorithms with three-dimensional diffusion models for property-driven molecular generation. The method operates through adaptive evolutionary optimization, where population-based selection guides the generation process toward desired property landscapes. EvoDiffMol supports both unconstrained molecular design and scaffold-constrained generation that preserves fixed substructures while optimizing complementary regions. Comprehensive evaluation demonstrates exceptional performance, achieving the highest drug-likeness score (0.94) among all compared state-of-the-art methods while maintaining excellent validity, uniqueness, and novelty. Beyond single property optimization, the framework demonstrates flexible multi-property optimization capabilities, simultaneously controlling multiple molecular descriptors including synthetic accessibility, lipophilicity, topological polar surface area, and clinically relevant ADMET properties such as cardiotoxicity (hERG) and intestinal permeability (Caco-2). This adaptability spans from simple descriptors to practical pharmaceutical endpoints without requiring complete model retraining. The framework achieves precise control over target property values, generating molecules with properties closely matching specified targets for both single and multiple descriptors. Scaffold-constrained experiments preserve fixed molecular cores while maintaining effective property optimization. The three-dimensional representation offers advantages in maintaining structural validity during iterative optimization, with potential for geometry-aware applications in materials science and drug discovery.

3D molecular generation↗

DOE OSTI2026

Community-Informed Urban Flood Modeling for Impact Mitigation

Climate change is intensifying the hydrologic cycle, leading to more frequent and severe rainfall-driven (pluvial) flooding in urban areas. In the mid-Atlantic US cities, aging and under-designed stormwater infrastructure is increasingly strained by these events, resulting in recurring damage to property and disruptions to transportation networks. In this study, we combine community engagement with hydrologic modeling to develop and evaluate potential urban flood adaptation strategies. Over a three-year period, local technical experts and community representatives met regularly to discuss flooding concerns, identify priorities, and co-develop adaptation strategies. These discussions informed the development of an urban flooding model (EPA Storm Water Management Model) for the Baltimore Harbor watershed, the focus location of this study. The flooding model integrates complex surface and subsurface stormwater infrastructure data, local expert knowledge, and community insights. We simulate stakeholder-prioritized adaptations, such as green and gray infrastructure strategies. Model results demonstrate that enhanced infrastructure maintenance is the most effective adaptation for reducing flood depths, but has varied effects across the watershed, and can increase flooding in some locations. Spatially concentrated greening provides limited benefit to the watershed as a whole, but moderate benefit in community priority areas. Together, these adaptations have the potential to reduce flood depths by as much as 58% in some locations, greatly reducing property damage and mobility impacts, primary concerns of stakeholders. Future work will implement robust optimization tools to search for adaptations which meet stakeholder objectives and perform highly under varied future climate conditions. This work contributes to the expanding literature on collaborative modeling and demonstrates that community-engaged approaches can enhance model credibility and generate more actionable insights for communities seeking to strengthen climate resilience.

Baltimore MD↗

DOE OSTI2021

Active learning strategy for high fidelity short-term data-driven building energy forecasting

The quality of a data-driven model is heavily dependent on the quality of data. Data from building operation often have data bias problems, which means that the data sample is collected in a way that some members of the intended data population are less likely to be included than others. Data-driven energy forecasting models built on such data hence are biased and could lead to large forecasting errors. Active learning—an effective method to defying data bias—is rarely studied or applied in the area of data-driven building energy forecasting modeling. This paper attempts to fill this gap and explores the application of active learning in data-driven building energy forecasting. The developed strategy in this paper efficiently generate informative training data within a time budget and uses block design to passively consider weather disturbances. The developed active learning strategy is applied and evaluated in both virtual and real-building testbeds against traditional data-driven methods. Via these virtual and real-building evaluation cases, we have demonstrated that the data bias problem typically exists in building operation data is resolved by applying the developed active learning strategy. Furthermore, building energy forecasting models trained from data generated from the active learning strategy have shown improved performances in both model accuracy and model extendibility perspectives. The effectiveness of the block design module is also validated to effectively consider the impact of weather conditions on active learning design.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Composition design of high-entropy alloys with deep sets learning

High entropy alloys (HEAs) are an important material class in the development of next-generation structural materials, but the astronomically large composition space cannot be efficiently explored by experiments or first-principles calculations. Machine learning (ML) methods might address this challenge, but ML of HEAs has been hindered by the scarcity of HEA property data. In this work, the EMTO-CPA method was used to generate a large HEA dataset (spanning a composition space of 14 elements) containing 7086 cubic HEA structures with structural properties, 1911 of which have the complete elastic tensor calculated. The elastic property dataset was used to train a ML model with the Deep Sets architecture. The Deep Sets model has better predictive performance and generalizability compared to other ML models. Association rule mining was applied to the model predictions to describe the compositional dependence of HEA elastic properties and to demonstrate the potential for data-driven alloy design.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Data-Driven Optimal Control of Traffic Signals for Urban Road Networks

This paper studies the issue of data-driven optimal control design for traffic signals of oversaturated urban road networks. The signal control system based on the store and forward model is generally uncontrollable for which the controllable decomposition is needed. Instead of identifying the unknown parameters like saturation rates and turning ratios, a finite number of measured trajectories can be used to parametrize the system and help directly construct a transformation matrix for Kalman controllable decomposition through the fundamental lemma of J. C. Willems. On top of that, an infinite-horizon linear quadratic regulator (LQR) problem is formulated considering the constraints of green times for traffic signals. The problem can be solved through a two-phase data-driven learning process, where one solves an infinite-horizon unconstrained LQR problem and the other solves a finite-horizon constrained LQR problem. The simulation result shows the theoretical analysis is effective and the proposed data-driven controller can yield desired performance for reducing traffic congestion.

Liu, Tong↗

DOE OSTI2025

Large-scale offshore wind farm effects on weather and climate in Puerto Rico (Final Technical Report)

Puerto Rico’s current electricity generation heavily relies on imported fossil fuels. This results in an average cost of electricity higher than the U.S. mainland average in all sectors (residential, commercial, and industrial), despite abundant local offshore wind resources, which have the potential to provide secure, low-cost energy generation and consequent economic prosperity. However, effects on atmospheric and oceanic circulation resulting from large-scale deployments of offshore wind farms have not been previously studied at tropical latitude. This project addressed this knowledge gap through a computational modeling effort designed to capture the coupled dynamics of the atmosphere and the ocean in presence of offshore wind farms. Results indicate that wind farm wakes can alter wind stress, generate Ekman-driven vertical transport, and potentially affect nutrient distribution. While full model coupling remains challenging, progress in parameterization and large-eddy simulations provides a foundation for future research. The project contributes to DOE’s Earth System modeling efforts and supports STEM workforce development.

17 WIND ENERGY↗

DOE OSTI2026

Community-Engaged Modeling of Urban Flood Adaptation Pathways

Spangler, Ava [Pennsylvania State University] (ORC↗

DOE OSTI2022

Cyber-Resilient Design Methodology for Microgrids

Recent advancement in tools has helped with microgrid design, development, planning and operation. Microgrids offer a unique application based on users with different requirements for tools. The process of designing, constructing, commissioning, and assessing a microgrid is not always straightforward due to these distinct requirements. Additionally, metrics are needed for performance evaluation. This panel will offer an overview and description of tools that helps with microgrid design, construction, planning, operation, cyber security, and metrics-driven performance assessment driven by multiple diverse applications and use cases.

CCE↗

DOE OSTI2025

Mechanical separations of corn stover anatomical fractions in an integrated feedstock preprocessing system: An experimental and data-driven modeling study

High variabilities of material attributes in lignocellulosic biomass present risks for biofuel and biochemical productions and must be mitigated via preprocessing. Since almost no mechanical device is originally designed for processing biomass, how to operate existing apparatuses with efficient performance has not been investigated extensively. This work presents a study on an integrated screening and air classification to separate cobs and stalks from husks and leaves in corn stover. Prototype machine learning models were developed to assess the feasibility of predicting the process outcome based on the measurable parameters. The models trained upon limited experimental data rendered decent predictive accuracy of yield and purity. The experimental data and modeling results collectively suggest decreasing throughput leads to a higher purity. To the contrary, if throughput increases, a lower purity is likely. A possible trade-off between yield and purity of the separated streams indicates the need for optimal combinations of feedstock size, moisture, and throughput to achieve optimized separations. The results of this study also suggest the need to further improve model predictability by developing more accurate formulations for physics governing the integrated unit operations. To accomplish this, additional experimental data needs to be generated for model training.

09 - BIOMASS FUELS↗

DOE OSTI2026

NanoPSD: A software for automatic detection of Nano-Particle Shape Distribution in electron microscopy images

Accurate quantification of the size and morphology of nanoparticles from electron microscopy (EM) images is essential to understand growth mechanisms, surface reactivity, and functional behavior in nanoscale materials. Manual analysis remains slow, subjective, and difficult to reproduce in large datasets. We introduce NanoPSD (Nano-Particle Shape Distribution), an open-source and fully automated framework for quantitative particle detection and morphology analysis from EM images. NanoPSD integrates adaptive contrast enhancement, polarity-agnostic scale-bar detection, Optical Character Recognition (OCR)-based calibration, and classical segmentation via Otsu thresholding with morphological refinement. Particle contours are used to extract geometric descriptors, including equivalent circular diameter, aspect ratio, circularity, and solidity, enabling automated classification into spherical, rod-like, and aggregate morphologies. The framework supports both single-image and batch processing, generating publication-quality visualizations, LaTeX-ready tables, and structured comma-separated values (CSV) datasets. As a demonstration, we applied NanoPSD to plasma-synthesized nanoparticle samples diagnosed via transmission electron microscopy (TEM). The code produced statistically robust size and morphology distributions spanning a few to tens of nanometers with minimal user supervision. The pipeline demonstrates high reproducibility and scalability, processing large image collections with consistent calibration and output formatting. Its modular design enables seamless integration of future deep-learning-based segmentation models, providing a pathway toward intelligent, data-driven electron microscopy analysis.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Comprehensive framework for data-driven model form discovery of the closure laws in thermal-hydraulics codes

The two-phase two-fluid model is a basis of many thermal-hydraulics codes used in design, licensing, and safety considerations of nuclear power plants. Thermal-hydraulics codes rely on the closure laws to close the system of conservation equations and describe the interactions between phases. These laws, derived from years of experimental investigations, are semi-empirical correlations that lack generality and have a limited range of applicability. Increase of computational power, availability of new experiments, and development of high-fidelity simulations has increased the number of validation data. The discrepancies between the code predictions and the validation data are a great source of knowledge. Missing physics that are not included in the model but are important for the considered phenomena can be discovered by propagating the information from the experimental results through the model. Furthermore, physics-discovered data-driven model form (P3DM) methodology integrates available integral effect tests and separate effects tests to determine the necessary corrections to the model form of the closure laws. In contrast to existing calibration techniques, the methodology modifies the functional form of the closure laws. Based on the functional form of the correction, the missing physics that were not included in the original model can be discovered. The methodology provides the alternative to the machine learning approach, in which the model is discovered in the form of the intractable black-box relation. In this work, the methodology was applied to the CTF subchannel code to improve the prediction of the two-phase flow phenomena.

42 ENGINEERING↗

DOE OSTI2024

Automated AI-driven Molecular Design for Therapeutic Discovery

In recent years, artificial intelligence and machine learning (AI/ML) approaches have revolutionized the process of designing new therapeutics, enabling scientists to rapidly respond to emerging threats from various pathogens. A prime example is the SARS-CoV-2 main protease, a key target for the development of antiviral inhibitors. In this study, we employed a novel, integrated approach that combines AI-driven iterative design of inhibitor candidates, screening based on physio-chemical properties and toxicity, physics-based computational modeling of protein-inhibitor interactions, and AI-assisted analysis of Native MS biophysical assay and characterization of designed candidates. Our deep learning 3D-scaffold model, which uses an input scaffold as a starting point, generated tens of thousands of compounds while preserving the key scaffold. To optimize these candidates, we calculated a comprehensive set of 136 descriptors, including both 2D and 3D molecular features, for compounds targeting the SARS-CoV-2 Main protease (Mpro) and a neurodegenerative disease-associated protein, cyclophilin (Cyp). The generated compounds were initially filtered based on their properties and then ranked according to their predicted binding affinity using our automated modeling and ML methods. Experimental validation of the Mpro candidates showing inhibitory activity demonstrates that our workflow can expedite the therapeutic discovery.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗