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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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At least 73 records · Page 4
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

ML-Based Reconstruction in a Pixelated LArTPC

The Deep Underground Neutrino Experiment (DUNE) will address open issues in neutrino physics such as the measurement of the CP-violating phase in neutrino oscillations and the neutrino mass ordering. DUNE is expected to have the most energetic neutrino beam in the world. The 2x2 demonstrator is a single-phase liquid argon time projection chamber (LArTPC), with four modules, operated as a prototype for the DUNE Liquid Argon Near Detector (ND-LAr). Each module in the 2x2 demonstrator is 0.67m x 0.67m x 1.8m. Based on the ArgonCube design concept, the 2x2 features a novel pixelated charge readout and advanced high-coverage photon detection system. Machine learning can be used to form a complete reconstruction pipeline for physics interactions in the 2x2 detector. This poster will describe the workings of a package under development called SPINE and its current performance.

43 PARTICLE ACCELERATORS↗
DOE OSTI2025

Vibrational Signatures of Electronic Properties in Renewable-Energy Catalysis

The objective of this research program is to discover and develop new approaches for ab initio computational simulations of molecular motion. Specifically, this program aims to decipher the connection between the underlying electronic structure and the accordant molecular vibrations in reactive ions and radicals for renewable-energy purposes. Recent experimental progress in ion sources and optical spectroscopies has unearthed considerable new inner-sphere detail for these complexes, but the connection between these spectral signatures and mechanistic information often remains elusive, to the continued frustration of experimentalists. For this purpose, new anharmonic vibrational frequency methods, along with a publicly deployed software package, will be developed. Working closely with committed experimental collaborators, this conceptual and computational framework will be used to explain the results of new spectroscopy experiments, focusing specifically on the inner-shell mechanisms of renewable-energy catalysis. The oxidation half of catalytic water-splitting chemistry will be a central focus, along with fundamental studies of the manner in which strong ions and radicals activate solvent as a chemical species. The resulting products of the research program will include openly available software and algorithms for the ab initio simulation of challenging vibrational spectra, as well as critical mechanistic insight into energy-focused catalytic processes that are opaque to other existing analytical techniques.

42 ENGINEERING↗
DOE OSTI2024

HTESP (High-throughput electronic structure package): A package for high-throughput ab initio calculations

High-throughput ab initio calculations are the indispensable parts of data-driven discovery of new materials with desirable properties, as reflected in the establishment of several online material databases. The accumulation of extensive theoretical data through computations enables data-driven discovery by constructing machine learning and artificial intelligence models to predict novel compounds and forecast their properties. Efficient usage and extraction of data from these existing online material databases can accelerate the next stage materials discovery that targets different and more advanced properties, such as electron–phonon coupling for phonon-mediated superconductivity. However, extracting data from these databases, generating tailored input files for different ab initio calculations, performing such calculations, and analyzing new results can be demanding tasks. Here, in this work, we introduce a software package named “HTESP” (High-Throughput Electronic Structure Package) written in Python and Bash languages, which automates the entire workflow including data extraction, input file generation, calculation submission, result collection and plotting. Our HTESP will help speed up future computational materials discovery processes.

36 MATERIALS SCIENCE↗
DOE OSTI2025

An Open-Source Python Package for CFD Solution Verification

Informed decision-making using computational fluid dynamics (CFD) results requires quantifying the errors and uncertainties of a simulation. Verification, validation, and uncertainty quantification (VVUQ) methods were developed to address this need and have matured. However, these VVUQ analyses are often non-trivial and require CFD analysts and practitioners to have specific skill sets. This has led to the uneven adoption of VVUQ analyses, in part, based on the availability of software tools to aid CFD analysts and practitioners. Solution verification, a procedure to evaluate the accuracy of a simulation by estimating potential errors arising from the computational model and computing the uncertainties without comparing to results from a physical system, is one of the lagging VVUQ analyses as the absence of software has forced CFD analysts and practitioners to develop their own codes or piece together incomplete software from across the internet. This work presents an opensource Python package, CFDverify, to lower the barrier of entry and fill in the technological gap in solution verification. CFDverify also provides a streamlined framework to remove some potential errors in post-processing CFD results. The hope is that CFDverify can improve the quality and quantity of CFD solution verification in scientific and research studies and attract interest in developing a communal tool. This paper describes the design, features, and an example use of CFDverify.

Weinmeister, Justin [ORNL] (ORCID:0000000160090237↗
DOE OSTI2024

Optimizing Batch Crystallization with Model-based Design of Experiments

Poster for Foundations of Computer Aided Process Design (FOCAPD) 2024 describing the formality of model-based design of experiments for batch crystallization. This paper focuses on the implementation of model-based design of experiments for parameter precision, enabled by Pyomo.DoE, a package in Python. Specifically, this work outlines an identifiability analysis for the estimability of the model parameters in a batch crystallization system.

Lynch, Hailey↗
DOE OSTI2026

Biodegradable Biocomposite Development for Large Scale Additive Manufacturing

Oak Ridge National Laboratory (ORNL) worked with Mitsubishi Chemical America, Inc. to develop a highly biodegradable ~100% biobased feedstock for large format additive manufacturing (AM). Together, they developed an AM feedstock based on the biobased polyester, polybutylene succinate (PBS), that is typically used in agriculture and packaging projects. The motivation for this work was to leverage the high degradability of this polymer in AM to create a new avenue for end-of-life disposal of the product. A composite feedstock was developed from PBS, polylactic acid (PLA), and wood flour (WF). While WF improves printability, reduces feedstock cost, and can improve stiffness, it also often results in a loss in tensile strength which can inhibit/limit applications of the material. To recover the loss in tensile strength suffered by including large quantities, up to 30 wt.% WF in the composite, a chain extender was used to compatibilize the different composite constituents with each other. The feedstock was used to 3D-print a demonstration mold for precast concrete catch basins and was tested by casting 750 lbs of concrete. The mold survived the demonstration. Aerobic degradation testing of the composite feedstock showed that it was highly degradable, 40% biodegradation after 90 days, compared to other incumbent biobased feedstocks.

100% biobased feedstock for large format additive ↗
DOE OSTI2026

Biodegradable Biocomposite Development for Large Scale Additive Manufacturing

Oak Ridge National Laboratory (ORNL) worked with Mitsubishi Chemical America, Inc. to develop a highly biodegradable ~100% biobased feedstock for large format additive manufacturing (AM). Together, they developed a AM feedstock based on the biobased polyester, polybutylene succinate (PBS), that is typically used in agriculture and packaging projects. The motivation for this work was to leverage the high degradability of this polymer in AM to create a new avenue for end-of-life disposal of the product. A composite feedstock was developed from PBS, polylactic acid (PLA), and wood flour (WF). While WF improves printability, reduces feedstock cost, and can improve stiffness, it also often results in a loss in tensile strength which can inhibit/limit application of the material. To recover the loss in tensile strength suffered by including large quantities, up to 30 wt%, WF in the composite a chain extender was used to compatibilze the different composite constituents with each other. The feedstock was used to 3D-print demonstration mold for precast concrete catch basins and was tested by casting 750 lbs of concrete. The mold survived the demonstration. Aerobic degradation testing of the composite feedstock showed that it was highly degradable, 40% biodegradation after 90 days, compared to other incumbent biobased feedstocks.

36 MATERIALS SCIENCE↗
DOE OSTI2025

JuTrack: A Julia package for auto-differentiable accelerator modeling and particle tracking

Efficient accelerator modeling and particle tracking are key for the design and configuration of modern particle accelerators. In this work, we present JuTrack, a nested accelerator modeling package developed in the Julia programming language and enhanced with compiler-level automatic differentiation (AD). With the aid of AD, JuTrack enables rapid derivative calculations in accelerator modeling, facilitating sensitivity analyses and optimization tasks. Here we demonstrate the effectiveness of AD-derived derivatives through several practical applications, including sensitivity analysis of space-charge-induced emittance growth, nonlinear beam dynamics analysis for a synchrotron light source, and lattice parameter tuning of the future Electron-Ion Collider (EIC). Through the incorporation of automatic differentiation, this package opens up new possibilities for accelerator physicists in beam physics studies and accelerator design optimization.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

NCAP: Noncanonical Amino Acid Parameterization Software for CHARMM Potentials

Noncanonical Amino Acids (NCAAs) provide numerous avenues for introduction of novel functionality to peptides and proteins. NCAAs can be incorporated through solid phase synthesis or genetic code expansion in conjugation with heterologous expression of the encoded protein modification. Due to the difficulty of synthesis, wide chemical space and lack of empirically resolved structures modeling the effects of NCAA mutation is critical for rational protein design. To evaluate the structural and functional perturbations NCAAs introduce we utilize molecular potentials that describe the forces in protein structure. Most potentials such as CHARMM are designed to model canonical residues but can be parameterized in include novel NCAAs. Here, in this work, we introduce NCAP a software package to generate CHARMM compatible parameters from quantum chemical calculation. Unlike currently available tools NCAP is designed to recognize NCAA structure and automatically bridge the gap between DFT calculations and potential parameters. For our software we discuss workflow, validation against canonical parameter sets and comparison to published NCAA-protein structures.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

pyRMG: A framework for high-throughput, large-cell DFT calculations on supercomputers

Exascale computing delivers the raw power to simulate ever larger and more chemically realistic systems, but realizing this potential requires codes that can efficiently use thousands of processors. Our real-space multigrid (RMG) density functional theory (DFT) code’s grid-decomposition approach scales nearly linearly with the number of graphics processing units (GPUs), even for simulations exceeding thousands of atoms. This scalability makes RMG a compelling tool for high-throughput DFT studies of materials that would otherwise be bottlenecked in other codes (for example, by global fast Fourier transforms in plane-wave DFT). However, the limited workflow infrastructure for RMG has thus far constrained its adoption to a small user community. In this work, we present pyRMG, a Python package designed to streamline the setup and execution of RMG DFT calculations. Built on the pymatgen and ASE (Atomic Simulation Environment) computational materials science Python packages, pyRMG automates input generation and convergence checking, and it integrates with modern job schedulers (e.g., Flux) on leadership-class platforms such as Frontier and Perlmutter. Here, we demonstrate pyRMG for a high-throughput study of strain effects in 2D 2L-Bi 2 Se 3 /2L-NbSe 2 heterostructures, which offers chemical insights into this system and shows that RMG-based workflows can converge with limited user intervention.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

pyDiSCaMB : enabling the use of multipolar scattering factors in Phenix

Multipolar scattering models, such as the transferable aspherical atom model, account for atomic chemical interactions and provide a more accurate representation of experimental data. However, the simpler independent atom model (IAM), which assumes non-interacting atoms, is the only model available in the most widely used macromolecular refinement programs. This is primarily because IAM offers a hard-to-beat combination of computational efficiency and modelling power at typical macromolecular resolutions. By contrast, more accurate multipolar modelling has historically been limited due to its computational cost and the absence of an interface between software capable of calculating structure factors and gradients based on multipolar models and software designed for macromolecular refinement. This work introduces pyDiSCaMB , a Python software package designed to integrate between the computational crystallography toolbox ( cctbx ) and the quantum crystallography library DiSCaMB ( Densities in Structural Chemistry and Molecular Biology ), thus enabling multipolar scattering models in Phenix 's toolkit. The implementation, features and capabilities of pyDiSCaMB are presented, the runtimes for the calculation of structure factor and target gradients with respect to atomic parameters are explored, and Fourier images of electrostatic potential, electron density and deformation maps are computed as illustrative examples. The pyDiSCaMB library will make multipolar modelling widely available to the structural biology community, potentially transforming refinement and model-building for both crystallography and cryogenic electron microscopy (cryoEM).

MATTS data bank↗
DOE OSTI2024

ASME Section III, Division 5, Class A 100,000-hour design data for Alloy 709

This report documents the 100,000 hour, Class A ASME design material data for Alloy 709 based on the extensive Department of Energy, Office of Nuclear Energy, Advanced Reactor Technologies qualification test campaign. This report includes design tensile properties, creep rupture data, allowable stresses, isochronous stress-strain curves, buckling charts, and a few additional miscellaneous pieces of design data. Companion work at Oak Ridge National Laboratory and Argonne National Laboratory provide design cyclic data --- fatigue charts and creep-fatigue diagrams --- and an inelastic constitutive model. This work substantially completes the ASME data package for the Alloy 709 Code Case, though the design data will continue to updated as the final tests finish. This report also compares the design performance of Alloy 709 against that of 316H stainless steel to provide a reference for the improved high temperature strength of 709 compared to a reference material for sodium fast reactor construction.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Examination of 3013 Containers Baseline Surface Features

The Surveillance and Monitoring Program at Los Alamos National Laboratory (LANL) was tasked with evaluating the baseline features of 3013 containers. This baseline is to be used as a basis for comparison for 3013 containers that had been packaged with corrosive plutonium materials. The LANL team evaluated an unwelded container, an unused welded container, and a welded container that had held plutonium metal without any corrosive impurities. These three containers had features with depths no larger than 6 µm and had similar depth distributions. The features observed in the baseline containers were all shallower than those seen in containers packaged with corrosive plutonium materials. This work establishes workflows for feature identification and measurement, as well as establishment of baseline data for future comparison with corroded containers.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2025

PSCF+: An Extended and Improved Open-Source Software Package for Polymer Self-Consistent Field Calculations

This work introduces PSCF+, a freely available open‑source software tool for modeling how block copolymers self‑assemble into ordered nanoscale structures. The software allows researchers to study a wide range of polymer models and interaction types, making it possible to directly compare theoretical predictions with molecular and simulation‑based studies in a consistent way. PSCF+ incorporates several recently developed numerical methods that significantly reduce computer memory requirements and speed up calculations, enabling efficient use of modern GPU hardware. Here, it also provides automated tools to calculate free‑energy landscapes and phase boundaries, which are essential for understanding and predicting material behavior. Together, these advances make PSCF+ a powerful and flexible platform for studying polymer self‑assembly and support the design of new polymeric materials for scientific and technological applications.

Chemical calculations↗
DOE OSTI2026

FAIRLinked: Data FAIRification Tools for Materials Data Science

FAIRLinked is a software package created to support the FAIRification of materials science data, ensuring proper alignment with FAIR principles: Findable, Accessible, Interoperable, and Reusable. It is built to be compatible with MDS-Onto, an ontology designed to capture the semantics of various types of materials data, enabling integration and sharing across different research workflows. The package is subdivided into three subpackages: InterfaceMDS, RDFTableConversion, and QBWorkflow. The first subpackage, InterfaceMDS allows users to search for terms using either string search or various filters, explore different domains and subdomains, and add terms to MDS-Onto. RDFTableConversion is used for serialization and deserialization of data from CSV into JSONLDs and vice versa in a way that captures the semantics of the data using MDS-Onto. Lastly, QBWorkflow is a serialization and deserialization workflow that incorporates RDF Data Cube vocabulary, useful for working with multidimensional datasets. By offering these packages, FAIRLinked lowers the barrier of creating FAIR, machine-actionable data for researchers in the materials science community.

FAIR↗
DOE OSTI2024

Multiphysics modeling of accelerators through code integration

This work aims to improve the ability of particle accelerator researchers to develop high-performance accelerator cavity designs by creating an overall multiphysics framework that integrates and couples existing application codes. This framework will allow accelerator researchers to build multiphysics models that will optimize cavity design, improve understanding of whole-device performance, and reduce the development and fabrication costs of accelerator research. We utilize the open-source VizSchema data standard as an intermediate data structure interface layer to standardize interfaces between individual application codes. VizScema is extensively documented online, and plugins for VizSchema are available for popular visualization packages, including VisIt and ParaView. Currently, the work focuses on coupling the EM field solver COMSOL and the electron gun code MICHELLE to allow COMSOL field-solve results to be seamlessly used by MICHELLE for particle-solve. Later work will extend this integration to include other fields, particles, and thermodynamics simulation codes.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Optimal Design of Food Packaging Considering Waste Management Technologies to Achieve Circular Economy

Plastic packaging plays a fundamental role in the food industry, avoiding food waste and facilitating food access. The increasing plastic production and the lack of appropriate plastic waste management technologies represent a threat to the environmental and human welfare. Therefore, there is an urgent need to identify sustainable packaging solutions. Circular economy (CE) promotes reducing waste and increasing recycling practices to achieve sustainability. In this work, we propose a CE framework based on multi-objective optimization, considering both economic and environmental impacts, to identify optimal packaging designs and waste management technologies. Using mixed-integer linear programming (MILP), techno-economic analysis (TEA), and life cycle assessment (LCA), this work aims to build the first steps in packaging design, informing about the best packaging alternatives and the optimal technology or technologies to process packaging waste. For the economic analysis, we consider the minimum increase in price (MIP) when adding recycling to the cost of each packaging solution, while for the environmental analysis, the greenhouse gas emissions impact was considered. A case study on ground coffee packaging is used to illustrate the proposed framework. The results demonstrate that the multilayer bag option is the most convenient when considering both the chosen economic and environmental impacts.

Life Cycle Analysis↗
DOE OSTI2024

A robust synthetic data generation framework for machine learning in high-resolution transmission electron microscopy (HRTEM)

Machine learning techniques are attractive options for developing highly-accurate analysis tools for nanomaterials characterization, including high-resolution transmission electron microscopy (HRTEM). However, successfully implementing such machine learning tools can be difficult due to the challenges in procuring sufficiently large, high-quality training datasets from experiments. In this work, we introduce Construction Zone, a Python package for rapid generation of complex nanoscale atomic structures which enables fast, systematic sampling of realistic nanomaterial structures and can be used as a random structure generator for large, diverse synthetic datasets. Using Construction Zone, we develop an end-to-end machine learning workflow for training neural network models to analyze experimental atomic resolution HRTEM images on the task of nanoparticle image segmentation purely with simulated databases. Further, we study the data curation process to understand how various aspects of the curated simulated data—including simulation fidelity, the distribution of atomic structures, and the distribution of imaging conditions—affect model performance across three benchmark experimental HRTEM image datasets. Using our workflow, we are able to achieve state-of-the-art segmentation performance on these experimental benchmarks and, further, we discuss robust strategies for consistently achieving high performance with machine learning in experimental settings using purely synthetic data. Construction Zone and its documentation are available at https://github.com/lerandc/construction_zone.

36 MATERIALS SCIENCE↗