Predicting Iron–Sulfur Cluster Redox Potentials: A Simple Model Derived from Protein Structures
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Min, Jiyeon (ORCID:000900068086095X)↗
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Results for “Structural Modeling”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Data-driven modeling of background radiation structure utilizing matrix profile in nuclear security
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Alamaniotis, Miltiadis↗
An idealized model for the crystal structure of intermetallic compounds isostructural with Mg 3 Cr 2 Al 18
Mg 3 Cr 2 Al 18 (abbreviated in this report as MCA) is the parent phase for a large class of intermetallic compounds that belong to the cubic crystal space group, $Fd\overline{3}m$. The purpose of this paper is to introduce an ideal, unrelaxed crystal structure for compounds isostructural with MCA. There are five distinct atomic sublattices in MCA compounds, which can be denoted, $A, B, C, D,$ and $E$. With this, a general description for MCA structures can be written as $A^{8a}_{1}B^{16c}_{2}C^{16d}_{2}D^{48f}_{6}E^{96g}_{12}$, where the superscripts represent the Wyckoff special equipoints associated with the various sublattices in MCA, and the subscripts indicate the contributions of each sublattice to the stoichiometry of one formula unit in an any given MCA structured compound. Sublattices D and E are where deviations from ideality occur in real, MCA-like compounds. This paper examines MCA bond lengths, nearest-neighbour polyhedral arrangements, 3-D sublattice crystal structures, 2-D atom tessellation patterns, and crystal chemical effects associated with atomic relaxations on the $D$ and $E$ sublattices. The ideal MCA crystal structure developed in this report provides an appropriate initial structure for use as input to crystal structure refinements of diffraction data for MCA-like phases being examined experimentally, or as input for computational, atomistic simulations of the structures of such compounds.
36 MATERIALS SCIENCE↗
A Dislocation Transport based Crystal Plasticity model to study Structure-Property relations in Polycrystalline Materials.
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Chakraborty, Subhendu↗
Surrogates for Valve-Controlled Pipe Flow: Accelerating Nuclear Reactor Design
Neural surrogate models are developed to replace expensive steady-state RANS CFD simulations for valve-controlled pipe flow in nuclear reactor design. Using parametric CFD data generated with MOOSE Pronghorn across a range of valve geometry and flow conditions, three approaches are compared: a POD-based reduced-order model, a structured UNet on a cylindrical grid, and unstructured models (DeepONet and BiStride MeshGraphNet) on nondimensionalized point clouds. POD achieves the highest accuracy (99%) with fast inference but requires storing all solution snapshots, while the DeepONet and BSMS-GNN both achieve ~89% accuracy at sub-second inference, with the BSMS-GNN offering superior geometric generalizability. These surrogates enable rapid ranking of candidate valve designs and can warm-start CFD solvers to accelerate convergence, supporting agentic design iteration on the Prometheus platform.
42 - ENGINEERING↗
Statistical generic design of glass and optimization: Selective review on oxide glasses
Designing a single glass composition for a multidimensional property space is challenging, and the difficulty increases with the number of design criteria. Traditionally, the task is accomplished using multiple statistical models that describe the relationships between composition (C) and property (P) values, i.e., C-P models. Recently, the structure (S)-property (P) statistical modeling has emerged as a complementary approach. The S-P modeling approach has also been shown to be a preferred method for modeling glass properties, particularly when a small data set is available, such as in single-component studies, or when strong nonlinearities exist between composition and properties. The combined model package, C-S-P, implements the concept of generic glass design, i.e., designing glass for performance by first selecting a specific or optimized set of glass network structural groups using S-P models and then transferring the designed structures (genes) to a particular composition using C-S models. This article reviews a set of supporting cases from the previous C-S-P modeling studies of phosphate, silicate, and borosilicate glasses, which are relevant for many critical commercial applications. The methodology for developing the statistical C-S-P database is presented, enabling the application of P?S?C to achieve a generic glass design and optimization, targeting multiple design criteria for both performance and processing properties simultaneously.
Network structure↗
DEMOS (Demographic Microsimulator Tool for Longitudinal Synthetic Population) [SWR-25-135] related to NLR SWR-26-076
The Demographic Microsimulator (DEMOS) is an agent-based simulation framework used to model the evolution of population demographic characteristics and lifecycle events, such as education attainment, marital status, and other key transitions. DEMOS modules are designed to capture the interdependencies between short-term and long-term lifecycle events, which are often influential in downstream transportation and land-use modeling. A key feature of DEMOS is its ability to track changes in an agent’s demographic status from year t to year t + 1. This structure allows the model to evolve populations over any user-defined time horizon. As a result, DEMOS is well suited for analyzing medium- and long-term transportation-related decisions, including household vehicle transactions (e.g., purchasing, selling, or replacing vehicles) and work location choices. Core features of DEMOS include the modeling of more than ten lifecycle events, behaviorally realistic patterns informed by long-running panel data, explicit representation of interdependencies among lifecycle processes, and a flexible, modular simulation architecture. A technical memorandum describing DEMOS is available here. The memorandum provides an overview of the framework’s functionality, model structure, input and output data, and its applications in transportation planning and broader policy analysis contexts. Interested readers are also encouraged to consult the paper listed below for additional details on the DEMOS methodology. Sun, Bingrong, Shivam Sharda, Venu M. Garikapati, Mohamed Amine Bouzaghrane, Juan Caicedo, Srinath Ravulaparthy, Isabel Viegas de Lima, Ling Jin, C. Anna Spurlock, and Paul Waddell. "Demographic Microsimulator for Integrated Urban Systems: Adapting Panel Survey of Income Dynamics to Capture the Continuum of Life." Transportation Research Record (2025): 03611981251333339.
Sun, Bingrong [National Laboratory of the Rockies ↗
A Bayesian Multi-fidelity Neural Network to Predict Nonlinear Frequency Backbone Curves
The use of structural mechanics models during the design process often leads to the development of models of varying fidelity. Often low-fidelity models are efficient to simulate but lack accuracy, while the high-fidelity counterparts are accurate with less efficiency. Here, this paper presents a multi-fidelity surrogate modeling approach that combines the accuracy of a high-fidelity finite element model with the efficiency of a low-fidelity model to train an even faster surrogate model that parameterizes the design space of interest. The objective of these models is to predict the nonlinear frequency backbone curves of the Tribomechadynamics Research Challenge benchmark structure which exhibits simultaneous nonlinearities from frictional contact and geometric nonlinearity. The surrogate model consists of an ensemble of neural networks that learn the mapping between low and high-fidelity data through nonlinear transformations. Bayesian neural networks are used to assess the surrogate model's uncertainty. Once trained, the multi-fidelity neural network is used to perform sensitivity analysis to assess the influence of the design parameters on the predicted backbone curves. Additionally, Bayesian calibration is performed to update the input parameter distributions to correlate the model parameters to the collection of experimentally measured backbone curves.
42 ENGINEERING↗
Understanding and Modeling Pooled Rideshare Acceptance: Influential Factors, Preferred User Experiences, and Implications
This dissertation explores factors influencing pooled rideshare (PR) adoption to provide actionable insights for transportation network companies (TNCs) and policymakers. PR allows travelers to share rides with unknown passengers, offering benefits such as cost reduction and congestion relief. However, adoption remains limited due to safety concerns, privacy issues, and trust in rideshare platforms. A national U.S. survey with 5,385 respondents examined transportation preferences and barriers to PR adoption. Exploratory and confirmatory factor analyses identified five key factors influencing PR consideration—safety, service experience, privacy, traffic/environment, and time/cost. Second factor analyses examined ways to optimize PR experiences, revealing four factors—comfort/ease of use, convenience, vehicle technology/accessibility, and passenger safety. Privacy concerns, for instance, using regression analysis, were found to reduce the likelihood of PR adoption by 77%, and convenience had the potential to increase it by 156%. The Pooled Rideshare Acceptance Model (PRAM), based on the Technology Acceptance Model, assessed the impact of these factors using the Structural Equation Model (SEM). Privacy, safety, trust, and convenience had a large effect (Cohen's f2 > 0.35) on PR acceptance, while multigroup analyses (PRAMMA) explored 16 demographic variables such as gender, generation, and income, emphasizing the need for tailored strategies. Based on all the statistical analysis and workshops using descriptive statistics, 95 actionable recommendations were made from the riders' perspective. Findings highlight the importance of customized services, user experience improvements, and policy interventions to enhance PR adoption. This dissertation provides a roadmap for future research and policy development, ensuring evidence-based, practical strategies to improve PR services in the U.S. and beyond.
Gangadharaiah, Rakesh↗
Develop and verify soil/structure interaction for pile/foundation interaction
Phase II of the Offshore Code Comparison Collaboration, Continued, with Correlation and unCertainty (OC6) project was used to verify the implementation of a new soil-structure interaction (SSI) model for use within offshore wind turbine modeling software. The REDWIN Macro-element model implemented and verified in this study enables a computationally efficient way to model the linear and nonlinear SSI problem, including hysteretic damping, of a monopile structure. The modeling approach was integrated into several modeling tools and a series of increasingly complex simulations was conducted using the IEA 10MW reference turbine mounted on a monopile support structure to verify the coupling between the tools and the REDWIN Macro-element SSI model. This campaign includes only numerical verification between various software and modeling approaches so no experimental measurements are available. The load cases (LC) considered include: LC1 – static response of the tower and substructure LC2 – frequency and mode-shape analysis of the tower and substructure LC3 – response of the tower and substructure due to wind-only loading LC4 – response of the tower and substructure due to wave-only loading LC5 – response of the tower and substructure due to wind and wave loading. Detailed properties of the modeled system are found in the following reference, “Bergua, Roger, Amy Robertson, Jason Jonkman, and Andy Platt. 2021. "Specification Document for OC6 Phase II: Verification of an Advanced Soil-Structure Interaction Model for Offshore Wind Turbines.” Golden, CO: National Renewable Energy Laboratory. NREL/TP-5000-79938. https://www.nlr.gov/docs/fy21osti/79938.pdf. Details on the results from the OC6 Phase II project can be found in the following reference, “Bergua R, Robertson A, Jonkman J, et al. OC6 Phase II: Integration and verification of a new soil–structure interaction model for offshore wind design.” Wind Energy. 2022;25(5):793-810. doi:10.1002/we.2698
17 WIND ENERGY↗
Automated Framework for Groundwater Monitoring Using DWT with LSTM and Transformers
Environmental monitoring is critical for safeguarding public health and ecological well-being. Traditional data structuring and workflow monitoring methods consume significant time and effort, hindering timely insights and effective decision-making. Our study addresses this challenge by presenting an AI framework that automates data cleaning, structuring, and modeling processes, specifically targeting applications in groundwater monitoring. By leveraging automation for data processing and model training, our framework establishes a novel and efficient paradigm for environmental monitoring, with its potential application to the vast network of over a hundred Department of Energy Environmental Management (DoE-EM) cleanup sites across the country. It analyzes data streams from a network of groundwater Internet-of-Things (IoT) sensors deployed at the Savannah River Site (SRS) for prediction modeling. This allows human experts to focus on analysis and decision-making, ultimately leading to better environmental outcomes.The framework employs multivariate time-series forecasting methods to study and model the behavior of varying chemical analytes. The continuous learning process is enabled by utilizing deep learning techniques. It allows the framework to become more nuanced in its analysis over time, adapting to the specific characteristics of the environmental site and the evolving nature of contaminant behavior. Deep learning models known for sequence modeling, LSTM, and Transformers are employed for time series forecasting. Data processing and structuring are essential components significantly impacting the final model's performance. This hypothesis was proven by presenting a comparative analysis of model performance with processed and unprocessed data. The feature engineering approach utilized was the Discrete Wavelet Transform, which works well with time series data.
Discrete Wavelet Transform (DWT)↗
Quadrupolar NMR crystallography guided crystal structure prediction (QNMRX-CSP) of zwitterionic organic HCl salts
In this work, we benchmark quadrupolar NMR crystallography guided crystal structure prediction (QNMRX-CSP) for determining the crystal structures of two zwitterionic organic HCl salts, L-ornithine HCl ( Orn ) and L-histidine HCl·H 2 O ( Hist ). These salts present an interesting challenge for QNMRX-CSP, as gas-phase geometry optimizations used to generate starting structures for the organic zwitterionic fragments fail to capture their correct solid-state geometries. To overcome this limitation, geometry optimizations using the COSMO water-solvation model are employed to generate initial structural models. Using this approach, QNMRX-CSP yields structural models of the two zwitterionic organic HCl salts that closely match experimentally determined crystal structures. In addition, the application of QNMRX-CSP to Hist represents a further step toward the de novo structural determination of solvated organic HCl salts, as Hist is the first benchmark system of this type to include a water molecule as a component of its crystal structure. This work is significant for its potential application to the structural determination of active pharmaceutical ingredients, which often feature complex organic components and solvated solid forms.
Fleischer, Carl H. [Florida State Univ., Tallahass↗
Alabama Carbon Storage: Data Sharing and Engagement (Final Report)
This report is the final technical report on Alabama Carbon Storage: Data Sharing Engagement (ACS:DSE) project activities. The goals of the ACS:DSE project are to compile geologic, geophysical, infrastructure, and other relevant CCUS datasets for the study area and develop a geologic model of the study area; develop an online platform to serve data to stakeholders; engage with the public, students, and industry to educate them about CCUS and the data platform; and ensure energy and environmental justice is central to all aspects of the project. Datasets compiled and expanded include formation depths and elevations, digital geophysical well logs, reservoir properties, geologic structures, and geologic models. The geologic data were used to create a three-dimensional geologic model, structure grids, structure contour maps, and fault trace maps. In addition to downloadable datasets, links to CCUS relevant regulatory agencies (e.g., OGB, U.S. Environmental Protection Agency) and sources for infrastructure and educational information were included on the website Educational materials on CCUS for use by K-12 teachers were produced as part of the ACS:DSE project.
01 COAL, LIGNITE, AND PEAT↗
Isolated and H 2 -reduced Anderson clusters catalyse low-temperature hydrogenation of CO 2 to methanol
CO 2 hydrogenation, especially to methanol, is crucial to establishing sustainable closed-loop systems for carbon utilization. However, the difficulties of CO 2 activation at low temperatures and the ambiguity of structure–activity correlations are obstacles to reducing the energy consumption of the hydrogenation process. Here we report that molecularly defined Anderson PtMo 6 O 24 clusters, sited within a robust metal–organic framework, are catalytic for low-temperature CO 2 hydrogenation. The performance of the cluster showed no signs of decay in either its activity or methanol selectivity over 3,600 h at 180 °C. It also achieves a per-pass yield exceeding that of state-of-the-art heterogeneous catalysts under similar conditions. Combined in situ spectroscopy and density functional theory calculations demonstrated that CH 3 OH formation is dominated by the reverse water–gas shift and subsequent CO* hydrogenation pathway, while the HCOO* pathway may serve as a supplementary route. The well-defined cluster structure offers an ideal model for elucidating structure–activity correlations and opens exciting avenues for the rational design of high-activity, low-temperature catalysts for CO 2 hydrogenation.
heterogeneous catalysis↗
Chromatin structures from integrated AI and polymer physics model
The physical organization of the genome in three-dimensional space regulates many biological processes, including gene expression and cell differentiation. Three-dimensional characterization of genome structure is critical to understanding these biological processes. Direct experimental measurements of genome structure are challenging; computational models of chromatin structure are therefore necessary. We develop an approach that combines a particle-based chromatin polymer model, molecular simulation, and machine learning to efficiently and accurately estimate chromatin structure fromindirectmeasures of genome structure. More specifically, we introduce a new approach where the interaction parameters of the polymer model are extracted from experimental Hi-C data using a graph neural network (GNN). We train the GNN on simulated data from the underlying polymer model, avoiding the need for large quantities of experimental data. The resulting approach accurately estimates chromatin structures across all chromosomes and across several experimental cell lines despite being trained almost exclusively on simulated data. The proposed approach can be viewed as a general framework for combining physical modeling with machine learning, and it could be extended to integrate additional biological data modalities. Ultimately, we achieve accurate and high-throughput estimations of chromatin structure from Hi-C data, which will be necessary as experimental methodologies, such as single-cell Hi-C, improve.
Biochemistry & Molecular Biology↗
3-D Geological Modeling for Numerical Flow Simulation Studies of Gas Hydrate Reservoirs at the Kuparuk State 7-11-12 Pad in the Prudhoe Bay Unit on the Alaska North Slope
Accurate reservoir evaluation requires reliable three-dimensional (3-D) geological models. Here, this study conducted 3-D geological modeling for numerical flow simulation of the B1 sand gas hydrate reservoir at the Kuparuk State 7-11-12 pad, Prudhoe Bay Unit, Alaska North Slope. The model integrates well logs, core, and seismic data to address spatial heterogeneity in geological structures and reservoir properties. Two modeling types were performed: structural framework modeling and petrophysical property modeling. For structural framework modeling, seismic data and well log markers were used to reproduce subsurface structures characterized by a normal fault system. A volume-based modeling algorithm and stair-stepping grid were applied. The resulting 3-D model comprised 2,640,000 grid cells across 264 layers, including seven fault grids. For petrophysical property modeling, total porosity was initially modeled using sequential Gaussian simulation with collocated cokriging. To reproduce the upward coarsening of the B1 sand, upscaled log-derived total porosity and a three-dimensional (3-D) trend depicting total porosity variation were used as primary and secondary data, respectively. Gas hydrate saturation distribution was modeled similarly, with secondary data from estimated porosity distribution and seismic-derived acoustic impedance map enhancing accuracy. Results indicate higher gas hydrate saturation in the upper part of the B1 sand and areas with higher acoustic impedance. Intrinsic permeability was modeled from the total porosity and clay-bound water volume, and effective permeability was derived from the gas hydrate saturation and intrinsic permeability distributions based on the “Tokyo model”. Effective permeability distributions were influenced by the total porosity, gas hydrate saturation, and intrinsic permeability. Within the same layer, higher gas hydrate saturation leads to decreased effective permeability. In total, 100 sets of multiple scenarios were prepared, providing input data for dynamic flow simulations to evaluate the effects of lateral heterogeneity in reservoir properties and the hydraulic characteristics of faults on production behavior for preassessment before the long-term production test.
58 GEOSCIENCES↗
E-scooter safety: How attitudinal factors influence risky behavior among shared e-scooter riders
In recent years, e-scooter usage for short-distance trips has grown rapidly. This surge in e-scooter use, combined with the high exposure of e-scooter riders to accident risk, has sparked concerns regarding e-scooter safety. Despite some studies focusing on e-scooter safety, little is known about how attitudinal factors lead e-scooter riders to engage in risky riding behaviors. In this paper, we developed a survey-based empirical model to identify the attitudinal factors influencing engagement in risky behaviors among e-scooter users. We used survey data collected from 420 shared e-scooter users in Chicago in 2022. The survey showed that 47.7% of respondents had experienced at least one collision or fall-off while riding e-scooters. We employed the Partial Least Squares Structural Equation Model (PLS-SEM) to examine the relationships between latent attitudinal factors and risky behavior engagement. Moreover, we conducted Permutation Multi-group Analysis (PMGA) to assess the moderating effect of socio-demographic factors within the estimated model. The findings suggest that riders’ unsafe riding attitude and riding confidence are the most influential factors shaping their risky behavior engagement. In addition, accident experience, infrastructure suitability, perceived enjoyment, traffic risk perception, and operational risk perception are among the other significant predictors. Among socio-demographic factors, gender, age, education, and car use frequency significantly influence riders’ engagement in risky behaviors. The results highlight the importance of infrastructure suitability and accident experience in analyzing e-scooter users’ riding behavior. The developed model advances our understanding of factors contributing to e-scooter riders’ risky behavior engagement. The findings offer valuable insights for policymakers and e-scooter vendors aiming to mitigate e-scooter users’ accident risk. Specifically, we recommend three safety countermeasures: (1) safety training programs to encourage a safer attitude, (2) practice-based initiatives to enhance riding confidence, and (3) infrastructure improvements, especially the expansion of bike lanes.
E-scooter↗
SASHIMI-SIDM: semi-analytical subhalo modelling for self-interacting dark matter at sub-galactic scales
We combine the semi-analytical structure formation model, SASHIMI, which predicts subhalo populations in collisionless, cold dark matter (CDM), with a parametric model that maps CDM halos to self-interacting dark matter (SIDM) halos. The resulting model, SASHIMI-SIDM, generates SIDM subhalo populations down to sub-galactic mass scales, for an arbitrary input cross section, in minutes. We show that SASHIMI-SIDM agrees with SIDM subhalo populations from high-resolution cosmological zoom-in simulations in resolved regimes. Crucially, we predict that the fraction of core-collapsed subhalos peaks at a mass scale determined by the input SIDM cross section and decreases toward higher halo masses, consistent with the predictions of gravothermal models and cosmological simulations. For the first time, we also show that the core-collapsed fraction decreases toward lower halo masses. While the dependence of the collapse time on mass and concentration implies such behaviour, our semi-analytical approach allows us to quantify and illustrate this trend clearly across the full mass spectrum of subhalos, including for subhalo masses below the resolution limit of any current cosmological SIDM simulation. As a proof of principle, we apply SASHIMI-SIDM to predict the boost to the local dark matter density and annihilation rate from core-collapsed SIDM subhalos, which can be enhanced relative to CDM by an order of magnitude for viable SIDM models. Thus, SASHIMI-SIDM provides an efficient and reliable tool for scanning SIDM parameter space and testing it with astrophysical observations. The code is publicly available at https://github.com/shinichiroando/sashimi-si.
cosmological simulations↗