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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

On the Prospect of Chemically Transferable Coarse-Grained Electronic Models for Soft Materials

Electronic coarse-graining (ECG) methods predict quantum-mechanical electronic properties directly from coarse-grained (CG) molecular configurations, enabling electronic predictions at mesoscale length scales. Here, we present a diagnostic assessment of the feasibility of chemically transferable ECG models across a broad polymer-relevant chemical space using all-atom, united-atom, and Martini-scale representations. While high-resolution ECG models achieve near-quantitative accuracy, we show that chemically transferable ECG at the Martini resolution fails because the CG force field does not sample the same configurational distribution of local molecular structure as that underlying the DFT-parameterized ECG model. We demonstrate that our proposed Element-Count-Label (ECL) representation, which augments Martini beads with explicit stoichiometric data, significantly improves chemical generalization across diverse polymer chemistries. However, we find that even with improved chemical resolution, the model cannot recover electronic property distributions that are absent from the configurational space sampled by the CG force field. These results demonstrate that chemically transferable ECG requires future Martini-like force fields to explicitly preserve quantum chemistry–compatible local molecular structure in addition to thermodynamic and structural fidelity.

Kidder, Katherine M [Department of Chemistry; Univ↗
DOE OSTI2022

Accurate and Rapid Forecasts for Geologic Carbon Storage via Learning-Based Inversion-Free Prediction

Carbon capture and storage (CCS) is one approach being studied by the U.S. Department of Energy to help mitigate global warming. The process involves capturing CO 2 emissions from industrial sources and permanently storing them in deep geologic formations (storage reservoirs). However, CCS projects generally target “green field sites,” where there is often little characterization data and therefore large uncertainty about the petrophysical properties and other geologic attributes of the storage reservoir. Consequently, ensemble-based approaches are often used to forecast multiple realizations prior to CO 2 injection to visualize a range of potential outcomes. In addition, monitoring data during injection operations are used to update the pre-injection forecasts and thereby improve agreement between forecasted and observed behavior. Thus, a system for generating accurate, timely forecasts of pressure buildup and CO 2 movement and distribution within the storage reservoir and for updating those forecasts via monitoring measurements becomes crucial. This study proposes a learning-based prediction method that can accurately and rapidly forecast spatial distribution of CO 2 concentration and pressure with uncertainty quantification without relying on traditional inverse modeling. The machine learning techniques include dimension reduction, multivariate data analysis, and Bayesian learning. The outcome is expected to provide CO 2 storage site operators with an effective tool for timely and informative decision making based on limited simulation and monitoring data.

58 GEOSCIENCES↗
DOE OSTI2020

Landsat near-infrared (NIR) band and ELM-FATES sensitivity to forest disturbances and regrowth in the Central Amazon

Forest disturbance and regrowth are key processes in forest dynamics, but detailed information on these processes is difficult to obtain in remote forests such as the Amazon. We used chronosequences of Landsat satellite imagery (Landsat 5 Thematic Mapper and Landsat 7 Enhanced Thematic Mapper Plus) to determine the sensitivity of surface reflectance from all spectral bands to windthrow, clear-cut, and clear-cut and burned (cut + burn) and their successional pathways of forest regrowth in the Central Amazon. We also assessed whether the forest demography model Functionally Assembled Terrestrial Ecosystem Simulator (FATES) implemented in the Energy Exascale Earth System Model (E3SM) Land Model (ELM), ELM-FATES, accurately represents the changes for windthrow and clear-cut. The results show that all spectral bands from the Landsat satellites were sensitive to the disturbances but after 3 to 6 years only the near-infrared (NIR) band had significant changes associated with the successional pathways of forest regrowth for all the disturbances considered. In general, the NIR values decreased immediately after disturbance, increased to maximum values with the establishment of pioneers and early successional tree species, and then decreased slowly and almost linearly to pre-disturbance conditions with the dynamics of forest succession. Statistical methods predict that NIR values will return to pre-disturbance values in about 39, 36, and 56 years for windthrow, clear-cut, and cut + burn disturbances, respectively. The NIR band captured the observed, and different, successional pathways of forest regrowth after windthrow, clear-cut, and cut + burn. Consistent with inferences from the NIR observations, ELM-FATES predicted higher peaks of biomass and stem density after clear-cuts than after windthrows. ELM-FATES also predicted recovery of forest structure and canopy coverage back to pre-disturbance conditions in 38 years after windthrows and 41 years after clear-cut. The similarity of ELM-FATES predictions of regrowth patterns after windthrow and clear-cut to those of the NIR results suggests the NIR band can be used to benchmark forest regrowth in ecosystem models. Our results show the potential of Landsat imagery data for mapping forest regrowth from different types of disturbances, benchmarking, and the improvement of forest regrowth models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Measurement of the muon anomalous precession frequency in runs 4, 5, and 6 of the muon ${g}-2$ Experiment at Fermilab

The Fermilab E989 Muon $g-2$ experiment measures the muon's anomalous magnetic moment to a precision of 127 parts per billion, as reported in June 2025. The value is proportional to the difference between the muon's cyclotron frequency and the spin precession frequency in the presence of a uniform magnetic field, for muons contained within the $g-2$ storage ring. Spin precession frequency is extracted from the time distribution of the muon's decay positrons recorded by 24 electromagnetic calorimeters positioned around the inner circumference of the storage ring. The anomalous precession frequency is one of the primary experimental inputs necessary to estimate the anomalous magnetic moment, the other being the measurement of the magnetic field. This dissertation details the anomalous precession frequency extraction, including reconstruction, time-distribution fitting, and treatment of systematic uncertainties for the final three data-collection runs: Run-4, Run-5, and Run-6. This data represents a fourfold increase in statistics over the previous analysis release, halving the statistical uncertainty. The residual slow term from previous analyses is now well understood and documented in a systematic treatment. As of the writing of this dissertation, the theoretical prediction for the SM estimate of the muon's anomalous magnetic moment is under debate, with two competing prediction methods, so a definitive comparison with theory is not available. The results submitted for experimental release use the kernel-ratio asymmetry method, contributing 115 parts per billion to the statistical uncertainty and 34 parts per billion to the systematic uncertainty. When combined with the previous analyses in earlier data runs, this thereby improves the measurement beyond the experimental goal and sets the world's most precise measurement of the muon's anomalous magnetic moment.

Israel, Scott Nathan [Boston U.]↗
DOE OSTI2025

Measurement of the muon anomalous precession frequency in runs 4, 5, and 6 of the muon ${g}-2$ Experiment at Fermilab

The Fermilab E989 Muon $g-2$ experiment measures the muon's anomalous magnetic moment to a precision of 127 parts per billion, as reported in June 2025. The value is proportional to the difference between the muon's cyclotron frequency and the spin precession frequency in the presence of a uniform magnetic field, for muons contained within the $g-2$ storage ring. Spin precession frequency is extracted from the time distribution of the muon's decay positrons recorded by 24 electromagnetic calorimeters positioned around the inner circumference of the storage ring. The anomalous precession frequency is one of the primary experimental inputs necessary to estimate the anomalous magnetic moment, the other being the measurement of the magnetic field. This dissertation details the anomalous precession frequency extraction, including reconstruction, time-distribution fitting, and treatment of systematic uncertainties for the final three data-collection runs: Run-4, Run-5, and Run-6. This data represents a fourfold increase in statistics over the previous analysis release, halving the statistical uncertainty. The residual slow term from previous analyses is now well understood and documented in a systematic treatment. As of the writing of this dissertation, the theoretical prediction for the SM estimate of the muon's anomalous magnetic moment is under debate, with two competing prediction methods, so a definitive comparison with theory is not available. The results submitted for experimental release use the kernel-ratio asymmetry method, contributing 115 parts per billion to the statistical uncertainty and 34 parts per billion to the systematic uncertainty. When combined with the previous analyses in earlier data runs, this thereby improves the measurement beyond the experimental goal and sets the world's most precise measurement of the muon's anomalous magnetic moment.

Israel, Scott Nathan [Boston U.]↗
DOE OSTI2026

Measurement of the muon anomalous precession frequency in Runs 4, 5, and 6 of the Muon g-2 experiment at Fermilab

The Fermilab E989 Muon g − 2 experiment measures the muon’s anomalous magnetic moment to a precision of 127 parts per billion, as reported in June 2025. The value is proportional to the difference between the muon’s cyclotron frequency and the spin precession frequency in the presence of a uniform magnetic field, for muons contained within the g − 2 storage ring. Spin precession frequency is extracted from the time distribution of the muon’s decay positrons recorded by 24 electromagnetic calorimeters positioned around the inner circumference of the storage ring. The anomalous precession frequency is one of the primary experimental inputs necessary to estimate the anomalous magnetic moment, the other being the measurement of the magnetic field. This dissertation details the anomalous precession frequency extraction, including reconstruction, time-distribution fitting, and treatment of systematic uncertainties for the final three data-collection runs: Run-4, Run-5, and Run-6. This data represents a fourfold increase in statistics over the previous analysis release, halving the statistical uncertainty. The residual slow term from previous analyses is now well understood and documented in a systematic treatment. As of the writing of this dissertation, the theoretical prediction for the SM estimate of the muon’s anomalous magnetic moment is under debate, with two competing prediction methods, so a definitive comparison with theory is not available. The results submitted for experimental release use the kernel-ratio asymmetry method, contributing 115 parts per billion to the statistical uncertainty and 34 parts per billion to the systematic uncertainty. When combined with the previous analyses in earlier data runs, this thereby improves the measurement beyond the experimental goal and sets the world’s most precise measurement of the muon’s anomalous magnetic moment.

Israel, Scott Nathan [Boston U.]↗
DOE OSTI2023

A Method for Projecting Cloud Shadows Onto a Central Receiver Field to Predict Receiver Damage

This work demonstrates methods of mapping high-spatial-resolution direct normal irradiance (DNI) data from satellites, Total Sky Imagers (TSIs), and analogous data sources onto a heliostat field for characterizing the spatial and temporal variation of the incident flux on a central receiver tower during cloud transient events. The mapping methods are incorporated into an optical software module that interfaces with CoPylot–SolarPILOT’s python API– to provide computationally efficient optical simulation of the heliostat field and the solar power tower. Eventually, this optical model will be incorporated into optimization models whereby a plant operator can understand the effects of cloud transient events on overall power production and receiver lifetime due to creep-fatigue damage and therefore make better informed decisions about receiver shutdown events. By more accurately modelling the effects of cloud events on receiver flux maps, this work may determine the magnitude and frequency of thermal cycling on receiver tubes and panels using actual or realistic cloud shapes instead of averaged DNI values–which may undercount the total cycle number. This work may also prevent unnecessary plant shutdowns due to overly precautionary control strategies and characterize the relative impact of various cloud types on receiver life. We plan to eventually integrate this methodology into the System Advisor Model (SAM) to improve performance model accuracy during periods of cloudiness. In this paper, we demonstrate generating DNI maps and mapping them to a solar field in CoPylot using 10 m resolution data from publicly available Sentinel-2 satellite data over the Crescent Dunes plant.

Mullin, Matthew↗
DOE OSTI2024

E min : A First-Principles Thermochemical Descriptor for Predicting Molecular Synthesizability

Predicting the synthesizability of a new molecule remains an unsolved challenge that chemists have long tackled with heuristic approaches. Here, in this study, we report a new method for predicting synthesizability using a simple yet accurate thermochemical descriptor. We introduce E min , the energy difference between a molecule and its lowest energy constitutional isomer, as a synthesizability predictor that is accurate, physically meaningful, and first-principles based. We apply E min to 134,000 molecules in the QM9 data set and find that E min is accurate when used alone and reduces incorrect predictions of "synthesizable" by up to 52% when used to augment commonly used prediction methods. Our work illustrates how first-principles thermochemistry and heuristic approximations for molecular stability are complementary, opening a new direction for synthesizability prediction methods.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Assessment of DFT methods for the prediction of detachment energies and electronic structures of complex and multiply charged anions

Here, in this work, we assess the ability of different density functional theory methods to reproduce the vertical/adiabatic detachment energies (VDEs/ADEs) of a series of 40 anions, including monoanion versus multiply charged anions (MCAs) and covalent versus noncovalent interaction. The statistical analysis of errors is firstly performed by comparing the theoretical values with the experimental benchmark data obtained from the negative ion photoelectron spectroscopy (NIPES). The proposed optimally tuned range-separated (OTRS) functionals are proved to not only well reproduce the experimental VDEs/ADEs, but also simulate experimental NIPES. The result implies a more serious issue of delocalization error for MCAs and a balanced description of electron-rich/- deficient region of anions is necessary. The radius of the spherically symmetric average electron localization function (ELF) region is demonstrated as a useful descriptor for the characterization of electronic structure of anionic systems and a quasilinear fitting model is proposed to efficiently obtain their OTRS parameters.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

An Assessment of Homogeneous Mixture Method Cavitation Models in Predicting Cavitation in Nozzle Flow

The homogeneous mixture method (HMM) is a popular class of models used in the computational prediction of cavitation. Several cavitation models have been developed for use with HMM to govern the development and destruction of vapor in a fluid system. Two models credited to Kunz and Schnerr–Sauer are studied in this paper. The goal of this work is to provide an assessment of the two cavitation submodels in their ability to predict cavitation in nozzle flow. Validation data were obtained via experiments which employ both passive cavitation detection, (PCD) via acoustic sensing and optical cavitation detection (OCD) via camera imaging. The experiments provide quantitative information on cavitation inception and qualitative information on the vapor in the nozzle. The results show that initial vapor formation is not predicted precisely but within reason. A sensitivity analysis of the models to input parameters shows that the Schnerr–Sauer method does not depend upon the estimation of nuclei size and number density. Small changes in the vapor formation rate but not the total vapor volume can be seen when weighting parameters are modified. In contrast, changes to the input parameters for the Kunz model greatly change the final total vapor volume prediction. The assessment also highlights the influence of vapor convection within the method. Finally, the analysis shows that if the fluid and nozzle walls do not support nuclei larger than 40 μm, the methods would still predict cavitation when indeed there would be none in practice.

Engineering↗
DOE OSTI2020

Method for accurately predicting solvation structure

Accurately predicting the molecular structure of solutions is a fundamental scientific challenge. Using quantum mechanical density functional theory (DFT) to make these predictions is hindered by significant variation depending on which DFT functional is used. Here, we present a simple metric that can determine the reliability of a DFT functional for predicting solvation structure. We then show that including a simple interaction term to correct this metric leads to quantitative agreement with experimental measurements of liquid structure. We demonstrate the utility of this method by using it to accurately describe the hydration structure around the Na+ a

basis sets, molecular structure, cluster chemistry↗
DOE OSTI2023

Data-driven methods for diffusivity prediction in nuclear fuels

The growth rate of structural defects in nuclear fuels under irradiation is intrinsically related to the diffusion rates of the defects in the fuel lattice. The generation and growth of atomistic structural defects can significantly alter the performance characteristics of the fuel. This alteration of functionality must be accurately captured to qualify a nuclear fuel for use in reactors. Predicting the diffusion coefficients of defects and how they impact macroscale properties such as swelling, gas release, and creep is therefore of significant importance in both the design of new nuclear fuels and the assessment of current fuel types. In this article, we apply data-driven methods focusing on machine learning (ML) to determine various diffusion properties of two nuclear fuels—uranium oxide and uranium nitride. We show that using ML can increase, often significantly, the accuracy of predicting diffusivity in nuclear fuels in comparison to current analytical models. We also illustrate how ML can be used to quickly develop fuel models with parameter dependencies that are more complex and robust than what is currently available in the literature. In conclusion, these results suggest there is potential for ML to accelerate the design, qualification, and implementation of nuclear fuels.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2020

Survey of Modeling and Simulation Techniques for Advanced Manufacturing Technologies Volume I – Predicting Initial Microstructures

This report summarizes the current state of modeling and simulation methods for predicting the initial structure and properties of material in components assembled using advanced manufacturing technologies (AMTs). The report is the first volume in a two-volume series. This first volume focuses on predicting initial microstructures of AM material. The second volume discusses predicting material properties given the initial microstructure. The focus is on technologies of particular relevance to the design and manufacture of nuclear reactor components. The purpose of the report is to help develop the technical knowledge base to support regulatory decisions that will be needed to assess nuclear components manufactured with AMTs as these components are installed at nuclear power plants (NPPs). The report develops a list of AMTs of particular interest to the NRC and summarizes the key microstructural features and corresponding processing parameters relevant to each technology. Further sections describe available physically-based and data-driven prediction methods and survey widely available software tools. The report concludes with a summary of gaps that may be of particular interest to the NRC when evaluating modeling and simulation methods for AMTs.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Photo-zSNthesis: Converting Type Ia Supernova Lightcurves to Redshift Estimates via Deep Learning

Abstract Upcoming photometric surveys will discover tens of thousands of Type Ia supernovae (SNe Ia), vastly outpacing the capacity of our spectroscopic resources. In order to maximize the scientific return of these observations in the absence of spectroscopic information, we must accurately extract key parameters, such as SN redshifts, with photometric information alone. We present Photo- z SNthesis, a convolutional neural network-based method for predicting full redshift probability distributions from multi-band supernova lightcurves, tested on both simulated Sloan Digital Sky Survey (SDSS) and Vera C. Rubin Legacy Survey of Space and Time data as well as observed SDSS SNe. We show major improvements over predictions from existing methods on both simulations and real observations as well as minimal redshift-dependent bias, which is a challenge due to selection effects, e.g., Malmquist bias. Specifically, we show a 61× improvement in prediction bias 〈Δ z 〉 on PLAsTiCC simulations and 5× improvement on real SDSS data compared to results from a widely used photometric redshift estimator, LCFIT+Z. The PDFs produced by this method are well constrained and will maximize the cosmological constraining power of photometric SNe Ia samples.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Improved naive Bayesian probability classifier in predictions of nuclear mass

Recently, novel statistical methods such as neural networks and Bayesian learning methods are implemented to describe the nuclear masses. Based on previous studies, an improved naive Bayesian probability (iNBP) classifier is proposed to study the nuclear masses by refining the results of sophisticated nuclear models. In the iNBP method, the prediction for nuclear masses is treated as a classification problem. The residuals are classified into several groups to generate prior and conditional probabilities, and the posterior probabilities are further determined by the Bayesian formula. We choose the expectation with maximum probability as the final prediction. Reliability of the iNBP method is assessed by analyzing the global optimizations and the extrapolating capabilities. Here, the iNBP method exhibits impressive improvements on global descriptions for different mass models. Moreover, the method shows robust extrapolating capabilities. Results demonstrate the iNBP method can be applied to predict the nuclear masses of unknown regions. Considering the local mass relations, the iNBP method can offer considerable fine-tuning of the mass descriptions from nuclear models. The methodology proposed in this paper can also be applied to other model-based extrapolations of nuclear observables.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Evaluating the Trustworthiness of Explainable Artificial Intelligence (XAI) Methods Applied to Regression Predictions of Arctic Sea Ice Motion

Abstract Recent advances in explainable artificial intelligence (XAI) methods show promise for understanding predictions made by machine learning (ML) models. XAI explains how the input features are relevant or important for the model predictions. We train linear regression (LR) and convolutional neural network (CNN) models to make 1-day predictions of sea ice velocity in the Arctic from inputs of present-day wind velocity and previous-day ice velocity and concentration. We apply XAI methods to the CNN and compare explanations to variance explained by LR. We confirm the feasibility of using a novel XAI method [i.e., global layerwise relevance propagation (LRP)] to understand ML model predictions of sea ice motion by comparing it to established techniques. We investigate a suite of linear, perturbation-based, and propagation-based XAI methods in both local and global forms. Outputs from different explainability methods are generally consistent in showing that wind speed is the input feature with the highest contribution to ML predictions of ice motion, and we discuss inconsistencies in the spatial variability of the explanations. Additionally, we show that the CNN relies on both linear and nonlinear relationships between the inputs and uses nonlocal information to make predictions. LRP shows that wind speed over land is highly relevant for predicting ice motion offshore. This provides a framework to show how knowledge of environmental variables (i.e., wind) on land could be useful for predicting other properties (i.e., sea ice velocity) elsewhere. Significance Statement Explainable artificial intelligence (XAI) is useful for understanding predictions made by machine learning models. Our research establishes trustability in a novel implementation of an explainable AI method known as layerwise relevance propagation for Earth science applications. To do this, we provide a comparative evaluation of a suite of explainable AI methods applied to machine learning models that make 1-day predictions of Arctic sea ice velocity. We use explainable AI outputs to understand how the input features are used by the machine learning to predict ice motion. Additionally, we show that a convolutional neural network uses nonlinear and nonlocal information in making its predictions. We take advantage of the nonlocality to investigate the extent to which knowledge of wind on land is useful for predicting sea ice velocity elsewhere.

Hoffman, Lauren [Scripps Institution of Oceanograp↗