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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 sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Municipal Fleet Action Plan Guide

This action plan template and guide have been designed to support light-duty or medium-duty municipal fleet managers as they plan to adopt electric vehicles. The resource is an adaptation of content created by the National Laboratory of the Rockies (NLR), with support from World Resources Institute (WRI), for participants in the U.S. Department of Energy's Energy to Communities peer-learning cohort, Charting a Path to Municipal Fleet Electrification. Fifteen participating municipalities joined monthly cohort workshops from July through December 2024 to learn and plan for their own fleet electrification. Clean Cities and Communities coalitions supported participating municipalities throughout the cohort series by conducting fleet analysis and planning activities on their behalf. This template is an adapted version of cohort activities.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

Predicting Energetics Materials’ Crystalline Density from Chemical Structure by Machine Learning

To expedite new molecular compound development, a long-sought goal within the chemistry community has been to predict molecules’ bulk properties of interest a priori to synthesis from a chemical structure alone. In this work, we demonstrate that machine learning methods can indeed be used to directly learn the relationship between chemical structures and bulk crystalline properties of molecules, even in the absence of any crystal structure information or quantum mechanical calculations. We focus specifically on a class of organic compounds categorized as energetic materials called high explosives (HE) and predicting their crystalline density. An ongoing challenge within the chemistry machine learning community is deciding how best to featurize molecules as inputs into machine learning models—whether expert handcrafted features or learned molecular representations via graph-based neural network models—yield better results and why. We evaluate both types of representations in combination with a number of machine learning models to predict the crystalline densities of HE-like molecules curated from the Cambridge Structural Database, and we report the performance and pros and cons of our methods. Our message passing neural network (MPNN) based models with learned molecular representations generally perform best, outperforming current state-of-the-art methods at predicting crystalline density and performing well even when testing on a data set not representative of the training data. However, these models are traditionally considered black boxes and less easily interpretable. Here, to address this common challenge, we also provide a comparison analysis between our MPNN-based model and models with fixed feature representations that provides insights as to what features are learned by the MPNN to accurately predict density.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Final Report for CSP Tower Public Opinion and Education Project

As part of the CSP Plant Optimization Study for the California Power Market (DE-EE0009809) the project wanted to understand the public’s opinion of the technology and and explore the types of community engagement that is needed to support such development. The initial objectives for the public perception activities were twofold. The primary objective was to gather public opinion and feedback from the communities living near the two operating solar power tower plants, Ivanpah and Crescent Dunes, and to distill lessons learned from the community engagement conducted before, during, and after the plants were developed to inform future development. This included outreach to nearby airports. The other objective was to gauge public opinion about large-scale solar, specifically CSP towers, to start educating the public on the benefits and to begin building relationships with communities of interest, initially targeting the Kingman, Arizona area. The objectives shifted after the first exploratory trip to Ivanpah and Kingman, however, as it became apparent that it would be challenging to gather public opinion from the community surrounding Ivanpah that could be useful for other community profiles, and the company wasn’t ready to address the concerns in Kingman. Another area was chosen, therefore, to represent those where future development is possible. The recently published Lawrence Berkeley National Lab Perceptions of Large-Scale Solar Project Neighbors Study exemplified public perception polling based on social science and served as the foundation for the survey questions taken to the field. The intent was to ensure that people knew their input was valued and that the time they spent was valuable for the participant as well. It has been noted in the literature that in-person interaction has greater benefits than activities online or via mail, as well as limits the expense.

14 SOLAR ENERGY↗
DOE OSTI2022

Physics Community Needs, Tools, and Resources for Machine Learning

Machine learning (ML) is becoming an increasingly important component of cutting-edge physics research, but its computational requirements present significant challenges. In this white paper, we discuss the needs of the physics community regarding ML across latency and throughput regimes, the tools and resources that offer the possibility of addressing these needs, and how these can be best utilized and accessed in the coming years.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Understanding and Leveraging the I/O Patterns of Emerging Machine Learning Analytics

The scientific community is currently experiencing unprecedented amounts of data generated by cutting-edge science facilities. Soon facilities will be producing up to 1 PB/s which will force scientist to use more autonomous techniques to learn from the data. The adoption of machine learning methods, like deep learning techniques, in large-scale workflows comes with a shift in the workflow’s computational and I/O patterns. These changes often include iterative processes and model architecture searches, in which datasets are analyzed multiple times in different formats with different model configurations in order to find accurate, reliable and efficient learning models. This shift in behavior brings changes in I/O patterns at the application level as well at the system level. These changes also bring new challenges for the HPC I/O teams, since these patterns contain more complex I/O workloads. In this paper we discuss the I/O patterns experienced by emerging analytical codes that rely on machine learning algorithms and highlight the challenges in designing efficient I/O transfers for such workflows. We comment on how to leverage the data access patterns in order to fetch in a more efficient way the required input data in the format and order given by the needs of the application and how to optimize the data path between collaborative processes. We will motivate our work and show performance gains with a study case of medical applications.

Gainaru, Ana↗
DOE OSTI2022

Applications and Techniques for Fast Machine Learning in Science

In this community review report, we discuss applications and techniques for fast machine learning (ML) in science—the concept of integrating powerful ML methods into the real-time experimental data processing loop to accelerate scientific discovery. The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for training and implementing performant and resource-efficient ML algorithms; and computing architectures, platforms, and technologies for deploying these algorithms. We also present overlapping challenges across the multiple scientific domains where common solutions can be found. This community report is intended to give plenty of examples and inspiration for scientific discovery through integrated and accelerated ML solutions. This is followed by a high-level overview and organization of technical advances, including an abundance of pointers to source material, which can enable these breakthroughs.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Trust and consequences: Role of community science, perceptions, values, and environmental justice in risk communication

Risk communication is often viewed as imparting information and perhaps as a two-way dialogue. Risk communication inadequacies on the part of both “communicator” and “community members” can lead to adverse consequences and amplify environmental justice disparities. The paper suggests a transformational approach where risk communicators must learn to trust community experts and their knowledge base (and act upon it), where risk information imparted by risk communicators addresses what communities are most concerned about (as well as risk from specific chemicals or radionuclides), and where risk information and assessments address underlying issues and disparities, as well as cultural traditions (among others). Providing risk probabilities is no longer sufficient; western science may not be enough, and community and native scientific knowledge is needed. Risk communication (or information transfer) for environmental risks that are ongoing usually applies to low-income, minority communities—people living in dense inner cities, rural communities, Native American communities—or to people living near a risky facility. Communication within this context requires mutual trust, listening and respect, as well as acceptance of indigenous and community knowledge as equally valuable. Examples are given to illustrate a community perspective.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Overview of Community Solar Project Development [Slides]

The Energy to Communities (E2C) peer-learning cohort program provides technical assistance to groups of 15 community entities around a common energy topic over the course of 6 months. Every month, participants join a virtual meeting where they hear from experts and exchange strategies and best practices with their peers. This cohort, "Successful Energy Project Implementation" will explore common challenges in implementing energy projects and learn strategies to turn project plans into reality. Each participant will focus on a local energy project or priority as a cornerstone for their learning throughout the series. This presentation overviews the basics of developing community solar projects with emphasis on technical scope and ownership structures.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Improved representation of black carbon mixing structures suggests stronger direct radiative heating

Black carbon significantly influences the Earth system because of its strong solar radiation absorption. However, its direct radiative effect remains poorly understood in current climate models, partly because current climate models oversimplify the diverse structures formed when black carbon mixes with other atmospheric components. Here we show that incorporating more realistic, multi-mixing-structure representations of black carbon increases the direct radiative effect. We find that aged black carbon particles, with thicker coatings and higher embedded fractions, enhance the direct radiative effect more efficiently. Using machine learning alongside the Community Earth System Model, we show that the direct radiative effect at the top of the atmosphere in regions with heavy black carbon pollution is 31.6% greater when multi-mixing structures are considered. These findings highlight the importance of modeling complex mixing structures of particle-resolved black carbon to accurately capture their warming impacts on global atmosphere, particularly in highly polluted regions.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

An Introductory Guide to Generating, Using, and Storing Energy: Understanding electricity, energy storage, microgrids, and related technologies

This guide was created to empower Energy Storage for Social Equity (ES4SE) community members to learn more about power systems, energy distribution, renewable energy, and battery storage systems. It was developed in an accessible format for audiences of varied backgrounds and requires no previous experience in energy storage. It is intended to serve readers as both an educational and reference document as they consider, procure, and deploy power systems that include battery energy storage technologies and microgrids in their communities. By providing an introductory overview of the basics of electricity, power generation, and household energy consumption and loads, this guide prepares readers to learn even more about battery energy storage, microgrid systems, and the sizing of solar and battery systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2026

Energy Project Siting, Permitting, and Community Impacts [Slides]

The Energy to Communities (E2C) peer-learning cohort program provides technical assistance to groups of 15 community entities around a common energy topic over the course of 6 months. Every month, participants join a virtual meeting where they hear from experts and exchange strategies and best practices with their peers.This cohort, "Successful Energy Project Implementation" will explore common challenges in implementing energy projects and learn strategies to turn project plans into reality. Each participant will focus on a local energy project or priority as a cornerstone for their learning throughout the series. This presentation focuses on strategies for successful project financing. This workshop is on April 29, 2026.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Pathways Toward Electric Mobility: Communities LEAP in Hennepin County, Minnesota

This report presents the findings from a comprehensive community engagement initiative focused on advancing equitable transportation electrification in Hennepin County. This initiative responds to the critical need for inclusive and sustainable mobility solutions in the face of rising environmental and financial challenges in the transportation sector. This project has two goals. First, to learn what each community's priorities are and share them with the city, county, and state government to inform transportation plans and projects. Second, to bridge divides between government and community, building pathways for community-guided decision making on future transportation plans.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

Pathways Toward Electric Mobility in Hennepin County, Minnesota

This fact sheet presents the findings from a comprehensive community engagement initiative focused on advancing equitable transportation electrification in Hennepin County. This initiative responds to the critical need for inclusive and sustainable mobility solutions in the face of rising environmental and financial challenges in the transportation sector. This project has two goals. First, to learn what each community's priorities are and share them with the city, county, and state government to inform transportation plans and projects. Second, to bridge divides between government and community, building pathways for community-guided decision making on future transportation plans.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗