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At least 1,261 records · Page 70

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DOE OSTI2025

Probing topological phases in a perturbed Kane-Mele model via RKKY interaction: Application to monolayer jacutingaite Pt 2 ⁢HgSe 3

Quantum spin Hall insulators (QSHIs) leverage strong spin-orbit coupling (SOC) for efficient spin manipulation, making them promising for spintronics. Here, in this study, we investigate the noncollinear Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction between two magnetic impurities in a perturbed Kane-Mele model with strong SOC, relevant to monolayer jacutingaite Pt 2 ⁢HgSe 3 as a prominent QSHI. Following the previous studies that mainly focused on the model and its general applications, we provide a systematic examination of the effects of various perturbations and strong spin-orbit hybridizations, which drive phase transitions that have not been extensively explored before. By incorporating these perturbations into the model and accurately accounting for spin-orbit hybridizations through spin-space Green's functions and the RKKY interactions, we uncover distinct, relative (rather than absolute) signatures of different phase transitions. These phase transitions are induced by both static and dynamic perturbations on the magnetic impurities. Notably, we identify additional phases emerging from the interplay with the magnetic substrate. All these influence the switching between ferromagnetic and antiferromagnetic, as well as clockwise and counterclockwise magnetic interactions. Our results provide a practical way to track topological phases through magnetic properties, offering new insights into phase control and spin manipulation in QSHIs.

Kane-Mele model↗

DOE OSTI2025

Enhancing Gaussian Process Surrogates for Optimization and Posterior Approximation via Random Exploration

This paper proposes novel noise-free Bayesian optimization strategies that rely on a random exploration step to enhance the accuracy of Gaussian process surrogate models. The new algorithms retain the ease of implementation of the classical GP-UCB algorithm, but the additional random exploration step accelerates their convergence, nearly achieving the optimal convergence rate. Furthermore, to facilitate Bayesian inference with intractable likelihoods, we propose to utilize optimization iterates for maximum a posteriori estimation to build a Gaussian process surrogate model for the unnormalized log-posterior density. We provide bounds for the Hellinger distance between the true and the approximate posterior distributions in terms of the number of design points. We demonstrate the effectiveness of our Bayesian optimization algorithms in nonconvex benchmark objective functions, in a machine learning hyperparameter tuning problem, and in a black-box engineering design problem. The effectiveness of our posterior approximation approach is demonstrated in two Bayesian inference problems for parameters of dynamical systems.

Bayesian inference↗

DOE OSTI2025

Computational Modeling to Advance Novel Medical Isotopes for Radiotheranostics: A DOE-NIH Joint Workshop Executive Summary

The DOE-NIH Joint Workshop on Computational Modeling to Advance Novel Medical Isotopes for Radiotheranostics, held on September 27, 2024, brought together experts from government, academia, and industry to address critical challenges in radionuclide production and clinical translation. Here, the workshop emphasized interdisciplinary collaboration, particularly between the Department of Energy (DOE) and the National Institutes of Health (NIH), to strengthen the domestic isotope supply, streamline regulatory pathways, and further integrate computational tools into radiopharmaceutical therapy (RPT). Key discussions explored the role of AI-driven modeling, machine learning, and digital twin technologies in optimizing dosimetry, dynamically personalizing treatments, and reducing time to clinical adoption. Advances in predictive computational modeling were highlighted as essential for improving radionuclide yield, purity, and synthesis efficiency. Regulatory considerations and equitable access were central themes, with participants advocating for harmonized global standards, adaptive trial designs, and expanded infrastructure for clinical implementation. DOE computational and production infrastructure was emphasized. Future priorities identified include increased investment in radionuclide production infrastructure, expanded workforce development in radiopharmaceutical sciences and computational modeling, and the creation of robust public-private partnerships. The workshop concluded that continued strategic collaboration and sustained resources will be vital for advancing next-generation radiotheranostics, ensuring safe and effective therapies accessible to all patients.

digital twins↗

DOE OSTI2021

The Encounter of the Parker Solar Probe and a Comet-like Object Near the Sun: Model Predictions and Measurements

The Parker Solar Probe (PSP) aims to explore the nascent solar wind close to the Sun. Meanwhile, PSP is also expected to encounter small objects like comets and asteroids. Here, we survey the ephemerides to find the chance of a recent encounter and then model the interaction between released dusty plasmas and solar wind plasmas. On 2019 September 2, a comet-like object, the 322P/Solar and Heliosphere Observatory, just passed its perihelion flying to a heliocentric distance of 0.12 au and swept by PSP at a relative distance as close as 0.025 au. We present the dynamics of the dust particles released from 322P, forming a curved dust tail. Along the path of PSP in the simulated inner heliosphere, the states of plasma and magnetic field are sampled and illustrated, with the magnetic field sequences from simulation results being compared directly with the in situ measurements from PSP. Through the comparison, we suggest that 322P might be at a deficient activity level releasing limited dusty plasmas on its way to becoming a "rock comet." We also present images of solar wind streamers as recorded by the Wide-field Imager for Solar Probe Plus, showing an indication of dust bombardment for the images superposed with messy trails. We observe from the Large Angle and Spectrometric Coronagraph that 322P was transiting from a dimming region to a relatively bright streamer during its perihelion passage, and perform a simulation to confirm that 322P was flying from relatively faster to slower solar wind streams, modifying the local plasma states of the streams.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2025

Critical impact of experimentally-driven strut level anisotropic material models in advanced stress analysis of additively manufactured lattice structures

The rapid acceleration in materials discovery may overshadow the importance of thoroughly understanding the mechanical performance of newly developed materials in demanding environments. The recent interest in combining parametric studies with machine learning techniques to explore how changes in specific processing parameters or model inputs affect the overall behavior of a material system can only be truly beneficial if the governing constitutive relations describing material behavior are accurately established. In this study, we demonstrate the critical impact of accurately representing strut-level anisotropic material behavior in advanced stress analysis of additively manufactured lattice structures (AMLS). We introduce a systematic experimental and modeling approach for developing strut-level anisotropic elastoplastic material models that account for the influence of microstructural features such as porosity, texture, and surface roughness on the development of local anisotropic mechanical properties, which vary with strut orientation relative to the build direction (BD). As a result the presented material model captures and relates the statistics of spatially varying struts’ microstructural features to the local stress distribution. Our findings suggest that incorporating strut-level anisotropic material behavior into unit cell analysis significantly influences the load distribution and evolution of local stresses within the structure. Therefore, accounting for this anisotropy is critical for developing an understanding of unit cell behavior and performance, including subsequent topology/component design optimization based on this analysis.

Sahoo, Subhadip [University of Arizona]↗

DOE OSTI2023

SULI Oral Presentation

Furthering our understanding of the prevalence and severity of issues that customers face when charging their electric vehicles (EVs) is crucial in order to improve the charging experience across the United States. This project utilizes web-scraping, machine leaning (ML), and natural language processing (NLP) techniques to analyze and categorize user-generated reviews. Selenium was used to build a data collection tool that can scrape vast amounts of user review data from the PlugShare website. Sentiment analysis was employed on this dataset in order to filter out negative reviews for further analysis. NLP techniques such as tokenization and word embedding were then used to convert user-written comments into a numerical format that a ML model can interpret. Multiple ML approaches are currently being explored in order to identify and categorize the charging issues being talked about in each review. Ultimately, the results from the ML model will be visualized and explained in a report on customer pain points to be delivered to the ChargeX Consortium, therefore revealing specific areas for improvement in the customer charging experience.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2023

SULI Oral Presentation

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

DOE OSTI2025

Interface morphology and dislocation-mediated processes during rapid solidification of thin films

Rapid solidification experiments have, in recent years, revealed a wealth of new microstructural phenomena that suggest a strong connection between the kinetics of solidification and the crystalline structures that emerge as a result. In this work, we investigate the interplay between interface morphology and defect-mediated processes during rapid solidification conditions using a Phase Field Crystal (PFC) model, enabling us to simultaneously and efficiently explore the physics of solidification and elasto-plasticity in the formalism of a single-field theory. We predict that there are two mechanisms by which dislocations emitted directly from the solid–liquid interface induce orientation gradients as well as the formation of subgrain boundaries within a single solidifying cell. We relate these mechanisms to the morphology of the moving solid–liquid interface and identify a suitable control parameter in the PFC model with which we can go between said morphologies by effectively changing the relative strength of the capillary length and kinetic coefficients of the solid–liquid interface. Thus, we are able to provide mechanistic explanations for several microstructural features (with an emphasis on orientation gradients and subgrain boundaries) observed during the rapid solidification of pure materials. We also provide a simple explanation for the formation of “jagged” subgrain boundaries, which is consistent with our experimental observations in rapidly solidified samples of Aluminum, whose mechanisms have thus far been unknown.

Interface morphology↗

DOE OSTI2022

Natural and anthropogenic controls on lake water‐level decline and evaporation‐to‐inflow ratio in the conterminous United States

Abstract Lake water levels are integral to lake function, but hydrologic changes from land and water management may alter lake fluctuations beyond natural ranges. We constructed a conceptual model of multifaceted drivers of lake water levels and evaporation‐to‐inflow ratio (Evap : Inflow). Using a structural equation modeling framework, we tested our model on (1) a national subset of lakes in the conterminous United States with minimal water management to describe natural drivers of lake hydrology and (2) five ecoregional subsets of lakes to explore regional variation in water management effects. Our model fits the national and ecoregional datasets and explained up to 47% of variation in Evap : Inflow, 38% of vertical water level decline, and 79% of horizontal water level decline (littoral exposure). For lakes with minimal water management, Evap : Inflow was related to lake depth ( β = −0.31) and surface inflow ( β = −0.44); vertical decline was related to annual climate (e.g., precipitation β = −0.18) and water management ( β = −0.21); and horizontal decline was largely related to vertical decline ( β = 0.73) and lake morphometry (e.g., depth β = −0.18). Anthropogenic effects varied by ecoregion and likely reflect differences in regional water management and climate. In the West, water management indicators were related to greater vertical decline ( β = 0.38), whereas in the Midwest, these indicators were related to more stable and full lake levels ( β = −0.22) even during drought conditions. National analyses show how human water use interacts with regional climate resulting in contrasting impacts to lake hydrologic variation in the United States.

Fergus, C. Emi↗

DOE OSTI2021

Optimality versus reality: Closing the gap between renewable energy decision models and government deployment in the United States

Energy decision models are widely used to evaluate the technical and economic feasibility of renewable energy, as well as to help inform the deployment of these technologies. However, a gap exists between the optimal model solutions and what is deployed. This paper explores why these gaps exist in the public sector using the results of interviews with 20 federal, state, and city government agencies that have used the Renewable Energy Integration and Optimization (REopt™) model to inform energy decisions. We then propose adaptations to technical modeling capabilities, and communication of results, which may help increase clean energy deployment. This research may be useful to both analytical modelers and the organizations using such decision tools to inform policy, regulation, planning, and deployment of clean energy systems.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2022

Exploring Dependencies of Work Function on Topography and the Application to Micron-scale Field Emission Model for PIC-DSMC Simulations .

Abstract not provided.

Moore, Christopher↗

DOE OSTI2025

Fleet Algorithm Design for Pooled Rideshare: Integrating Human Factors, Simulation, and Optimization

This dissertation explores the study the integration of human factors modeling and rideshare fleet control algorithms. Pooled rideshare is a unique transportation mode offering that allows riders increased flexibility and accessibility over public transportation, and decreased cost relative to personal vehicles or traditional rideshare. Additionally, relative to personal vehicles, pooled rideshare offers reduced costs and options for those with difficulty obtaining transportation. Prior research in the space typically focused on modeling human behavior, or optimizing system performance, but a lack of integration of the concepts leads to unrealistic or underutilized outcomes. To tackle this problem, novel rideshare assignment, and repositioning strategies were designed and implemented in a simulation environment. Through a series of successive studies, improvements to current rideshare processes were identified, and beneficial outcomes for profitability, accessibility, and traffic were explored. Further, improved metrics to assess rideshare performance were designed and analyzed in the context of improved rideshare offerings. This research contributes to the field of transportation by tackling novel but pragmatic approaches to challenges facing the rideshare industry. Through the course of this dissertation, rideshares impacts on users, operators, and even regulators will be explored in detail. The justification behind the use of a simulation environment, a set of simulated regional models for testing, and the focus on realism and deployability is illustrated. The research identifies holes in potential markets for the use of both private, and public rideshare systems.

Paul, Joseph↗

DOE OSTI2023

Exploration of a Novel Technique for Waste Heat Recovery Through Molecular Dynamics: Influence of Wettability and Electric Field on Water and Water-Based Nanofluids

Most of the energy produced globally comes by way of a heat engine. The Carnot principle places a limit as to how thermodynamically efficient a heat engine can be. There is no heat engine that can be 100% thermodynamically efficient and as such a substantial proportion of all heat supplied to a heat engine is lost as waste heat. Waste heat therefore is a large energy source ready to be properly utilized. Herein, a novel approach for converting waste heat to electricity is discussed. It involves the use of the liquid to vapor phase change of a material dielectric (water) or electrolyte (nanofluid) in the embodiment of a capacitor for direct thermal to electrostatic energy conversion. While this method of waste heat recovery could potentially be added to the ever expanding portfolio of energy conversion techniques, a number of aspects must be addressed before it can be brought into practice. Water was seen as an ideal dielectric phase change material given its high relative permittivity ratio when in the liquid form as compared to its vapor form. However, given its short voltage holdoff time the phase change of water would need to occur rapidly. This brings up concerns of explosive boiling. Herein, molecular dynamics analysis into the explosive boiling behavior of thin water films gave more insight into how the interaction between the surface and liquid affected explosive boiling onset time. A Lennard-Jones potential with one interaction site and a Morse potential with three interaction sites between water and solid substrate were used. It was found generally that a stronger interaction between water film and substrate led to faster explosive boiling onset times but an increase in the number of interaction sites delayed explosive boiling, even at the same wettability (contact angle). Understanding changes in the density and enthalpy of vaporization of a liquid dielectric such as water in the presence of an electric field is of importance due to the electrostatic nature of the waste heat conversion method under consideration. Specifically, if both density and enthalpy of vaporization are increased, the thermodynamic efficiency of the waste heat conversion method under consideration is decreased. Electric field effects are explored herein via molecular dynamics using two water models, the TIP4P-Ew and SWM4-NDP. The SWM4-NDP model is polarizable while the TIP4P-Ew model is not, which allows for a determination of the importance of model polarizability (i.e. variation in water model dipole moment) on these two properties of water when subjected to an electric field. Herein it was found that both water models respond similarly in terms of density and vaporization enthalpy variance upon the introduction of an electric field. Comparison was also made to the pressure induced by the electric field (electrostriction pressure) by way of a density comparison and it was found that the predicted electrostriction pressure overestimates the pressure experienced by water. Water by itself has a high enthalpy of vaporization, which limits the efficiency of the newly proposed conversion method. Research both experimental and through simulation has shown that the vaporization enthalpy of nanofluids can be engineered via nanoparticle size and material selection. An avenue less explored is manipulating the enthalpy of vaporization by altering the interaction strength between the nanoparticles and the base fluid. In practice this could be achieved through the addition of coatings to the nanoparticles to alter their wettability to the base fluid. This was explored by using a Lennard-Jones potential and Morse potential to model the interaction between base fluid (water) and the nanoparticle. For nanoparticles 2nm in diameter and at weight percentages up to 6%, the change in vaporization enthalpy due to alterations of the interaction strength between nanoparticle and base fluid was not significant (less than a 1% difference) when compared to the effect of altering the weight percentage of nanoparticles in the nanofluid or introducing an electric field. However, the effect of wettability may still become important at other nanoparticle concentrations and sizes. In all, the studies presented here further the understanding of phase change and thermodynamic properties of water and water based nanofluids under an electrostatic field which will help inform the development of a novel approach to waste heat conversion. The reduction of waste heat will improve energy sustainability outlooks.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2021

Molecular imaging of plant-microbe interactions on the Brachypodium seed surface

Plant growth-promoting rhizobacteria (PGPR) play a crucial role in biological control and pathogenic defense on and with-in plant tissues, however the mechanism(s) by which plants associate with PGPR to elicit such beneficial effects needs further study. Here, we present high mass resolution time-of-flight secondary ion mass spectrometry (ToF-SIMS) imaging of Brachypodium distachyon (Brachypodium) seeds with and without exposure to two model PGPR, i.e., Gram-negative Pseudomonas fluorescens SBW25::GFP (P.) and Gram-positive Arthrobacter chlorophenolicus A6::GFP (A.). Delayed image extraction was used to image PGPB-treated seed sections to reveal morphological changes. ToF-SIMS spectral compari-son, principal component analysis (PCA), and two-dimensional (2D) imaging show that the selected PGPR have different effects on the host seed surface, resulting in changes in chemical composition and morphology. Metabolite prod-ucts and biomarkers, such as flavonoids, phenolic com-pounds, fatty acids, and indole-3-acetic acid (IAA), were ob-served on the PGPR-treated seed surfaces. These compounds had different distributions on the Brachypodium seed surface for the two PGPR, indicating that the different bacteria elicited distinct responses from the host. Our results illustrate that ToF-SIMS is an effective tool to study plant-microbe interac-tions and to provide insightful information with submicrome-ter lateral resolution of the chemical distributions associated with morphological features, potentially offering a new way to study the mechanisms underlying beneficial roles of PGPR.

PGPR, Brachypodium distachyon, ToF-SIMS, metabolit↗

DOE OSTI2021

Navigating Transition-Metal Chemical Space: Artificial Intelligence for First-Principles Design

Conspectus The variability of chemical bonding in open-shell transition-metal complexes not only motivates their study as functional materials and catalysts but also challenges conventional computational modeling tools. Here, tailoring ligand chemistry can alter preferred spin or oxidation states as well as electronic structure properties and reactivity, creating vast regions of chemical space to explore when designing new materials atom by atom. Although first-principles density functional theory (DFT) remains the workhorse of computational chemistry in mechanism deduction and property prediction, it is of limited use here. DFT is both far too computationally costly for widespread exploration of transition-metal chemical space and also prone to inaccuracies that limit its predictive performance for localized d electrons in transition-metal complexes. These challenges starkly contrast with the well-trodden regions of small-organic-molecule chemical space, where the analytical forms of molecular mechanics force fields and semiempirical theories have for decades accelerated the discovery of new molecules, accurate DFT functional performance has been demonstrated, and gold-standard methods from correlated wavefunction theory can predict experimental results to chemical accuracy. The combined promise of transition-metal chemical space exploration and lack of established tools has mandated a distinct approach. In this Account, we outline the path we charted in exploration of transition-metal chemical space starting from the first machine learning (ML) models (i.e., artificial neural network and kernel ridge regression) and representations for the prediction of open-shell transition-metal complex properties. The distinct importance of the immediate coordination environment of the metal center as well as the lack of low-level methods to accurately predict structural properties in this coordination environment first motivated and then benefited from these ML models and representations. Once developed, the recipe for prediction of geometric, spin state, and redox potential properties was straightforwardly extended to a diverse range of other properties, including in catalysis, computational “feasibility”, and the gas separation properties of periodic metal–organic frameworks. Interpretation of selected features most important for model prediction revealed new ways to encapsulate design rules and confirmed that models were robustly mapping essential structure–property relationships. Encountering the special challenge of ensuring that good model performance could generalize to new discovery targets motivated investigation of how to best carry out model uncertainty quantification. Distance-based approaches, whether in model latent space or in carefully engineered feature space, provided intuitive measures of the domain of applicability. With all of these pieces together, ML can be harnessed as an engine to tackle the large-scale exploration of transition-metal chemical space needed to satisfy multiple objectives using efficient global optimization methods. In practical terms, bringing these artificial intelligence tools to bear on the problems of transition-metal chemical space exploration has resulted in ML-model assessments of large, multimillion compound spaces in minutes and validated new design leads in weeks instead of decades.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Multimodal parameter spaces of a complex multi-channel neuron model

One of the most common types of models that helps us to understand neuron behavior is based on the Hodgkin–Huxley ion channel formulation (HH model). A major challenge with inferring parameters in HH models is non-uniqueness: many different sets of ion channel parameter values produce similar outputs for the same input stimulus. Such phenomena result in an objective function that exhibits multiple modes (i.e., multiple local minima). This non-uniqueness of local optimality poses challenges for parameter estimation with many algorithmic optimization techniques. HH models additionally have severe non-linearities resulting in further challenges for inferring parameters in an algorithmic fashion. To address these challenges with a tractable method in high-dimensional parameter spaces, we propose using a particular Markov chain Monte Carlo (MCMC) algorithm, which has the advantage of inferring parameters in a Bayesian framework. The Bayesian approach is designed to be suitable for multimodal solutions to inverse problems. We introduce and demonstrate the method using a three-channel HH model. We then focus on the inference of nine parameters in an eight-channel HH model, which we analyze in detail. We explore how the MCMC algorithm can uncover complex relationships between inferred parameters using five injected current levels. The MCMC method provides as a result a nine-dimensional posterior distribution, which we analyze visually with solution maps or landscapes of the possible parameter sets. The visualized solution maps show new complex structures of the multimodal posteriors, and they allow for selection of locally and globally optimal value sets, and they visually expose parameter sensitivities and regions of higher model robustness. We envision these solution maps as enabling experimentalists to improve the design of future experiments, increase scientific productivity and improve on model structure and ideation when the MCMC algorithm is applied to experimental data.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Temporal covariance structure of multi-spectral phenotypes and their predictive ability for end-of-season traits in maize

Vegetation indices (VIs) derived from multi-spectral imaging (MSI) platforms can be used to study properties of crop canopy, providing non-destructive phenotypes that could be used to better understand growth curves throughout the growing season. To investigate the amount of variation present in several VIs and their relationship with important end-of-season traits, genetic and residual (co)variances for VIs, grain yield and moisture were estimated using data collected from maize hybrid trials. The VIs considered were Normalized Difference Vegetation Index (NDVI), Green NDVI, Red Edge NDVI, Soil-Adjusted Vegetation Index, Enhanced Vegetation Index and simple Ratio of Near Infrared to Red (Red) reflectance. Genetic correlations of VIs with grain yield and moisture were used to fit multi-trait models for prediction of end-of-season traits and evaluated using within site/year cross-validation. To explore alternatives to fitting multiple phenotypes from MSI, random regression models with linear splines were fit using data collected in 2016 and 2017. Heritability estimates ranging from (0.10 to 0.82) were observed, indicating that there exists considerable amount of genetic variation in these VIs. Furthermore, strong genetic and residual correlations of the VIs, NDVI and NDRE, with grain yield and moisture were found. Considerable increases in prediction accuracy were observed from the multi-trait model when using NDVI and NDRE as a secondary trait. Finally, random regression with a linear spline function shows potential to be used as an alternative to mixed models to fit VIs from multiple time points.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Behavior, Energy, Autonomy, Mobility Modeling Framework (BEAM) v1.0

The Behavior, Energy, Autonomy, and Mobility (BEAM) model is an integrated, agent-based travel demand simulation framework. Individual agents express preferences through a utility- maximizing evolutionary algorithm that minimizes each individual’s cost and time spent traveling via diverse modal options, including the competition for scarce supply resources such as parking spaces and charging infrastructure. BEAM simulates the essential elements that compose a dynamic transportation system. From the road network, parking and charging infrastructure, to the transit system and a synthetic population with plans and preferences, the virtual system is an amalgamation of multiple spatially resolved layers that together represent an integrated transportation system. BEAM is an extension to the MATSim (Multi-Agent Transportation Simulation) model, where agents employ reinforcement learning across successive simulated days to maximize their personal utility through plan mutation (exploration) and selecting between previously executed plans (exploitation). The BEAM model shifts some of the behavioral emphasis in MATSim from across-day planning to within- day planning, where agents dynamically respond to the state of the system during the mobility simulation. In BEAM, agents can plan across all major modes of travel including driving, walking, biking, transit, and demand-responsive ride hailing. It is designed to integrate with other open source transportation models, such as ActivitySim.

Lazarus, Jessica↗