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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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All sources NASA NTRS DOE OSTI

DOE OSTI2024

In Silico Chemical Experiments in the Age of AI: From Quantum Chemistry to Machine Learning and Back

Computational chemistry is an indispensable tool for understanding molecules and predicting chemical properties. However, traditional computational methods face significant challenges due to the difficulty of solving the Schrödinger equations and the increasing computational cost with the size of the molecular system. In response, there has been a surge of interest in leveraging artificial intelligence (AI) and machine learning (ML) techniques to in silico experiments. Integrating AI and ML into computational chemistry increases the scalability and speed of the exploration of chemical space. However, challenges remain, particularly regarding the reproducibility and transferability of ML models. This review highlights the evolution of ML in learning from, complementing, or replacing traditional computational chemistry for energy and property predictions. Starting from models trained entirely on numerical data, a journey set forth toward the ideal model incorporating or learning the physical laws of quantum mechanics. This paper also reviews existing computational methods and ML models and their intertwining, outlines a roadmap for future research, and identifies areas for improvement and innovation. Ultimately, the goal is to develop AI architectures capable of predicting accurate and transferable solutions to the Schrödinger equation, thereby revolutionizing in silico experiments within chemistry and materials science.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

Finalizing Transition to the New Data Center at BNL

Computational science, data management and analysis have been key factors in the success of Brookhaven National Laboratory's scientific programs at the Relativistic Heavy Ion Collider (RHIC), the National Synchrotron Light Source (NSLS-II), the Center for Functional Nanomaterials (CFN), and in biological, atmospheric, and energy systems science, Lattice Quantum Chromodynamics (LQCD) and Materials Science, as well as our participation in international research collaborations, such as the ATLAS Experiment at Europe's Large Hadron Collider (LHC) at CERN (Switzerland) and the Belle II Experiment at KEK (Japan). The construction of a new data center is an acknowledgement of the increasing demand for computing and storage services at BNL in the near term and enable the Lab to address the needs of the future experiments at the High-Luminosity LHC at CERN and the Electron-Ion Collider (EIC) at BNL in the long term. The Computing Facility Revitalization (CFR) project is aimed at repurposing the former National Synchrotron Light Source (NSLS-I) building as the new data center for BNL. The construction of the new data center was finished in 2021Q3, and it was delivered for production in early FY2022 for all collaborations supported by the Scientific Data and Computing Center (SDCC), including STAR, PHENIX and sPHENIX experiments at RHIC collider at BNL, the Belle II Experiment at KEK (Japan), and the Computational Science Initiative at BNL (CSI). This paper highlights the key mechanical, electrical, and networking components of the new data center in its final configuration as used in production since 2021Q4 and gives an overview for the extension of the central network systems into the new data center and the migration of a significant portion of IT load and services from the old data center to the new data center carried out in 20212023, with expected completion of the main phase of the gradual IT equipment replacement and migration from the old data center into the new one set to the end of FY2023 (Sep 30, 2023).

99 GENERAL AND MISCELLANEOUS↗

NASA NTRS2019

The OpenSSP Snow Particle and Scattering Property Database: Current Status and Future Plans

Understanding the connection between microphysical properties of falling snow and remote sensing observations requires detailed models of snow particles and the related electromagnetic scattering properties. To this end, we have created a database of synthetically-grown snow particles along with electromagnetic scattering properties calculated using advanced computational methods. We showcase the breadth of the catalog, current work, applications of the catalog, and future plans.

Adams, Ian S.↗

NASA NTRS2021

NASA / NIST / FAA Technical Interchange Meeting on Computational Materials Approaches for Qualification by Analysis for Aerospace Applications

This report documents the goals, organization and outcomes of a Technical Interchange Meeting (TIM) on Computational Materials Approaches for Qualification by Analysis, co-organized by NASA, NIST and the FAA. The TIM was held at NASA Langley Research Center on January 15-16, 2020. Approximately 60 subject matter experts (SMEs) representing 8 aerospace manufacturers, 7 government organizations and 2 universities participated. Expertise of the SMEs spanned the Technology Readiness Level (TRL) scale from the low-to-mid TRL focus of government laboratories and universities to the high TRL perspective of the regulatory organizations and aerospace manufacturers. During this TIM, the future needs of the government regulators and manufacturers motivated the overall discussion and framed the input given by the participants. Hence, the key objectives of the TIM were to understand existing gaps in model-based, e.g., computational materials, processing and performance predictions for aerospace materials and components and forecast how they can be matured to support material, process and part-level qualification and certification (Q&C). The TIM focused on process-intensive metallic materials technologies, including, but not limited to, additive manufacturing. Participation was roughly evenly divided among the processing and performance tracks, suggesting that both topic areas are generally perceived as being both valuable and requiring additional investment. The output of this TIM may be used by both participating and other organizations, in part, as guidance for future national efforts on maturing computational materials capabilities for use in the Q&C of advanced metallic material systems in aerospace applications.

Edward H Glaessgen↗

DOE OSTI2024

STITCHES: a Python package to amalgamate existing Earth system model output into new scenario realizations

Understanding the interaction between humans and the Earth system is a computationally daunting task, with many possible approaches depending on resources available and questions of interest. For example, state-of-the-art impact models require decade-long time series of relatively high frequency, spatially resolved and often multiple variables representing climatic impact-drivers (Ruane et al., 2022). Most commonly these are derived from the outputs of detailed, computationally expensive Earth System Models (ESMs) run according to a standard, limited set of future scenarios, the latest being the SSP-RCPs run under CMIP6/ScenarioMIP (Eyring et al., 2016; O’Neill et al., 2016). At the time of writing, O’Neill et al. (2016) has been cited more than 1750 times and Eyring et al. (2016) more than 5000 times, highlighting the broad, general applications of this data. Often, however, impact modeling seeks to explore new scenarios that were not part of the ScenarioMIP protocol, and/or needs a larger set of initial condition ensemble members than are typically available to quantify the effects of ESM internal variability. In addition, the recognition that the human and Earth systems are fundamentally intertwined, and may feature potentially significant feedback loops, is making integrated, simultaneous modeling of the coupled human-Earth system increasingly necessary, if computationally challenging with most existing tools (Thornton et al., 2017). For impact modelers, climate model emulators can be the answer to meet both the needs of: 1) creating realizations for novel scenarios and 2) achieving a simplified, computationally tractable representation of ESM behavior in a coupled human-Earth system modeling framework. We proposed a new, comprehensive approach to such emulation of gridded, multivariate ESM outputs for novel scenarios without the computational cost of a full ESM, STITCHES (Tebaldi et al., 2022). The approach outlined in Tebaldi et al. (2022) should be extensible to future CMIP eras, although the STITCHES software at present is strictly focused on CMIP6/ScenarioMIP data hosted on Pangeo (https://gallery.pangeo.io/repos/pangeo-gallery/cmip6/). The corresponding STITCHES Python package uses existing archives of ESMs’ scenario experiments from CMIP6/ScenarioMIP to construct gridded, multivariate realizations of new scenarios provided by reduced complexity climate models (Hartin et al., 2015; Meinshausen et al., 2011; Smith et al., 2018), or to enrich existing initial condition ensembles. Its output provides the same characteristics as the emulated ESM output: multivariate (spanning potentially all variables that the ESM has saved), spatially resolved (down to the native grid of the ESM), and preserving the same high frequency as the original data. A new realization of multiple variables can be generated on the order of minutes with STITCHES, rather than the hours or sometimes days that ESMs require.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Modeling Codes Used for LAMP

LAMP team is planning on using multiple computer codes as tools for modeling beam dynamics, electromagnetics, and plasma environment in ion sources. Below is a description of the computer tools we have been using or planning to use in the future. Most of the codes chosen for the LAMP project are capable to model most of the beam dynamics in LAMP. A judicious choice of the appropriate code to use will be based on speed and fidelity of the modeling with faster calculations produced by simplifying some features.

43 PARTICLE ACCELERATORS↗

NASA NTRS2016

Direction and Integration of Experimental Ground Test Capabilities and Computational Methods

This paper groups and summarizes the salient points and findings from two AIAA conference panels targeted at defining the direction, with associated key issues and recommendations, for the integration of experimental ground testing and computational methods. Each panel session utilized rapporteurs to capture comments from both the panel members and the audience. Additionally, a virtual panel of several experts were consulted between the two sessions and their comments were also captured. The information is organized into three time-based groupings, as well as by subject area. These panel sessions were designed to provide guidance to both researchers/developers and experimental/computational service providers in defining the future of ground testing, which will be inextricably integrated with the advancement of computational tools.

Dunn, Steven C.↗

DOE OSTI2022

Functional characterization of prokaryotic dark matter: the road so far and what lies ahead

Eight-hundred thousand to one trillion prokaryotic species may inhabit our planet. Yet, fewer than two-hundred thousand prokaryotic species have been described. This uncharted fraction of microbial diversity, and its undisclosed coding potential, is known as the “microbial dark matter” (MDM). Next-generation sequencing has allowed to collect a massive amount of genome sequence data, leading to unprecedented advances in the field of genomics. Still, harnessing new functional information from the genomes of uncultured prokaryotes is often limited by standard classification methods. These methods often rely on sequence similarity searches against reference genomes from cultured species. This hinders the discovery of unique genetic elements that are missing from the cultivated realm. It also contributes to the accumulation of prokaryotic gene products of unknown function among public sequence data repositories, highlighting the need for new approaches for sequencing data analysis and classification. Increasing evidence indicates that these proteins of unknown function might be a treasure trove of biotechnological potential. Here, we outline the challenges, opportunities, and the potential hidden within the functional dark matter (FDM) of prokaryotes. We also discuss the pitfalls surrounding molecular and computational approaches currently used to probe these uncharted waters, and discuss future opportunities for research and applications.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2023

Probing the thermal decomposition of plutonium (III) oxalate with IR and Raman spectroscopy, X-ray diffraction, and electron microscopy

The thermal decomposition of Pu(III) oxalate was analyzed by Raman microspectroscopy, infrared spectroscopy, scanning electron microscopy, and powder X-ray diffraction. These data show that crystalline Pu 2 (C 2 O 4 ) 3 •9H 2 O progressively loses water and oxalate ligands as it is heated, which leads to a decrease in long-range lattice ordering, though minimal changes are observed in gross crystalline morphology. The onset of PuO 2 formation was observed between 200 - 250 °C. Thermal decomposition of oxalate ligands leads to the formation of CO 2 and plutonium oxalate-carbonate moieties, which had not been observed in previously published thermogravimetric measurements of Pu(III) oxalate. Formation of plutonium oxalate-carbonate moieties is believed to be associated with a change in the plutonium oxidation state from 3+ to 4+, which occurs prior to PuO 2 formation. The data provided herein demonstrate the rich spectroscopic nature of a rather underexplored, and technologically relevant, plutonium system. Ideally these results will further future investigations into the Pu(III) oxalate system both experimentally and computationally.

36 MATERIALS SCIENCE↗

DOE OSTI2018

Magnetism of new metastable cobalt-nitride compounds

The search for new magnetic materials with high magnetization and magnetocrystalline anisotropy is important for a wide range of applications including information and energy processing. There is only a limited number of naturally occurring magnetic compounds that are suitable. This situation stimulates an exploration of new phases that occur far from thermal-equilibrium conditions, but their stabilization is generally inhibited due to high positive formation energies. Here a nanocluster-deposition method has enabled the discovery of a set of new non-equilibrium Co–N intermetallic compounds. The experimental search was assisted by computational methods including adaptive-genetic-algorithm and electronic-structure calculations. Conventional wisdom is that the interstitial or substitutional solubility of N in Co is much lower than that in Fe and that N in Co in equilibrium alloys does not produce materials with significant magnetization and anisotropy. By contrast, our experiments identify new Co–N compounds with favorable magnetic properties including hexagonal Co 3 N nanoparticles with a high saturation magnetic polarization ($J_s$ = 1.28 T or 12.8 kG) and an appreciable uniaxial magnetocrystalline anisotropy ($K_1$ = 1.01 MJ m -3 or 10.1 Mergs per cm 3 ). This research provides a pathway for uncovering new magnetic compounds with computational efficiency beyond the existing materials database, which is significant for future technologies.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2017

Solution techniques for transient stability-constrained optimal power flow – Part II

Transient stability-constrained optimal power flow is an important emerging problem with power systems pushed to the limits for economic benefits, dense and larger interconnected systems, and reduced inertia due to expected proliferation of renewable energy resources. In this study, two more approaches: single machine equivalent and computational intelligence are presented. Also discussed are various application areas, and future directions in this research area. In conclusion, a comprehensive resource for the available literature, publicly available test systems, and relevant numerical libraries is also provided.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2017

Recent advances in modeling and simulation of the exposure and response of tungsten to fusion energy conditions

Under the anticipated operating conditions for demonstration magnetic fusion reactors beyond ITER, structural materials will be exposed to unprecedented conditions of irradiation, heat flux, and temperature. While such extreme environments remain inaccessible experimentally, computational modeling and simulation can provide qualitative and quantitative insights into materials response and complement the available experimental measurements with carefully validated predictions. For plasma facing components such as the first wall and the divertor, tungsten (W) has been selected as the best candidate material due to its superior high-temperature and irradiation properties. In this paper we provide a review of recent efforts in computational modeling of W both as a plasma-facing material exposed to He deposition as well as a bulk structural material subjected to fast neutron irradiation. We use a multiscale modeling approach –commonly used as the materials modeling paradigm– to define the outline of the paper and highlight recent advances using several classes of techniques and their interconnection. Here we highlight several of the most salient findings obtained via computational modeling and point out a number of remaining challenges and future research directions

fusion materials↗

DOE OSTI2017

MACORD: Online Adaptive Machine Learning Framework for Silent Error Detection

Future HPC systems with ever-increasing resource capacity (such as compute cores, memory and storage) may significantly increase the risks on reliability. Silent data corruptions (SDCs) or silent errors are one of the major sources that corrupt HPC execution results. Unlike fail-stop errors, SDCs are rather harmful and dangerous in that they cannot be detected by hardware. We propose an online machine-learning based silent data corruption detection framework (abbreviated as MACORD) for detecting SDCs in HPC applications. In particular, we comprehensively investigate the prediction ability of a multitude of machine-learning algorithms in our study, and enable the detector to automatically select the best-fit algorithms at runtime to adapt to the data dynamics. Our learning framework exhibits low memory overhead (less than 1%), since it takes only spatial features (i.e., neighboring data values for each data point in the current time step) into the training data. Experiments based on real-world scientific applications/benchmarks show that our framework can get the detection sensitivity (i.e., recall) up to 99% while the false positive rate is limited to 0.1% in most cases, which is one order of magnitude improvement compared with the latest state-of-art spatial technique.

Subasi, Omer↗

DOE OSTI2025

Vision Foundation Models in Remote Sensing: A survey

Artificial intelligence (AI) technologies have profoundly transformed the field of remote sensing (RS), revolutionizing data collection, processing, and analysis. Traditionally reliant on manual interpretation and task-specific models, RS research has been significantly enhanced by the advent of foundation models (FMs)—large-scale pretrained AI models capable of performing a wide array of tasks with unprecedented accuracy and efficiency. This article provides a comprehensive survey of FMs in the RS domain. We categorize these models based on their architectures, pretraining datasets, and methodologies. Through detailed performance comparisons, we highlight emerging trends and the significant advancements achieved by those FMs. Additionally, we discuss technical challenges, practical implications, and future research directions, addressing the need for high-quality data, computational resources, and improved model generalization. Our research also finds that pretraining methods, particularly self-supervised learning (SSL) techniques like contrastive learning (CL) and masked autoencoders (MAEs), remarkably enhance the performance and robustness of FMs. This survey aims to serve as a resource for researchers and practitioners by providing a panorama of advances and promising pathways for the continued development and application of FMs in RS.

data models↗

DOE OSTI2018

Modeling Imperfect Generator Behavior in Power System Operation Models

A key component in power system operations is the use of computer models to quickly study and analyze different operating conditions and futures in an efficient manner. The output of these models are sensitive to the data used in them as well as the assumptions made during their execution. One typical assumption is that generators and load assets perfectly follow operator control signals. While this is a valid simulation assumption, generators may not always accurately follow control signals. This imperfect response of generators could impact cost and reliability metrics. This paper proposes a generator model that capture this imperfect behavior and examines its impact on production costs and reliability metrics using a steady-state power system operations model. Preliminary analysis shows that while costs remain relatively unchanged, there could be significant impacts on reliability metrics.

24 POWER TRANSMISSION AND DISTRIBUTION↗

DOE OSTI2025

IRIS-MASH: Efficient Multi-device Asynchronous Multi-Stream Heterogeneous Computing

In the rapidly evolving field of high-performance computing (HPC), effectively leveraging heterogeneous devices through asynchronous task programming is paramount. This paper presents a robust asynchronous task programming model tailored for a multi-device, multi-stream execution environment that incorporates a diverse array of heterogeneous computing units, including GPUs from various vendors and other accelerators. Current state-of-the-art task programming models provide methodologies to support asynchronous task executions, but they typically handle homogeneous devices using native programming languages, while support for heterogeneous devices is limited to frameworks like OpenCL. This gap presents significant challenges in abstracting heterogeneous devices to harness their true asynchronous capabilities effectively using their native programming languages. By implementing asynchronous task execution, our model significantly boosts the performance of tiled algorithm task graphs through overlapping data transfers with computation and enabling the simultaneous execution of multiple kernels. We integrate this approach into a heterogeneous Intelligent Runtime System (IRIS) and assess its performance using a suite of tiled algorithm benchmarks from the heterogeneous math kernels library (MatRIS) based on IRIS. Experimental results demonstrate a performance improvement ranging from 1.6 × to 2 × over IRIS without asynchronous support, and a notable 22% performance enhancement compared to established runtime systems such as StarPU and PaRSEC. This approach significantly improves computation efficiency of HPC workflows and provides a solid base for future exploration and development in the area of asynchronous task programming in heterogeneous systems.

Miniskar, Narasinga Rao [ORNL] (ORCID:000000018259↗

DOE OSTI2020

pnnl/capratTX

Thermoelectric Capacity at Risk Analysis Tool for ERCOT. capratTX is a unique R package, featuring algorithms for simulating water storages that provide cooling water for thermoelectric power plants. The tool may be used to compute long-term "capacity at risk" under a range of future climate projections.

Turner, Sean W D↗

DOE OSTI2024

scANN

SAND2025-00656O scANN, also known as sampling by coinflips artificial neural networks, is a software tool that estimates uncertainty in artificial intelligence by performing Monte Carlo analysis on the weight matrices of feedforward neural networks. This computationally intensive process aims to explore the potential value added by future probabilistic hardware. The program’s output helps researchers gain insights into how probabilistic neural networks work. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

SciDAC↗