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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 OSTI2024

MSD CoP Webinar: Using Meta-Repositories To Facilitate Open Science in MSD Research

Context: This webinar was hosted by the MultiSector Dynamics Community of Practice (MSD CoP; https://multisectordynamics.org). Abstract: In this webinar, we will describe the use of a GitHub meta-repository, for documenting and disseminating the tools and data supporting MSD publications. The goal is to make it easier for others to understand the flow of data and code through your experiment and to be able to reproduce your results and figures with only the information you have provided for them. The webinar will cover the role of open science in the MSD community, the origins and purpose of meta-repositories, and step-by-step instructions and best practices for building a meta-repository starting from the GitHub template (https://github.com/IMMM-SFA/metarepo). We will also discuss how to leverage MSD-LIVE (https://msdlive.org/) in your meta-repository, provide links to numerous examples you can learn from, and discuss the role of meta-repositories in the IM3 project's open science mandates. Presenters : Chris R. Vernon, Casey D. Burleyson, Jennie Rice, and Mengqi Zhao Moderator: Pat M. Reed (MSD CoP Facilitation Team) This webinar was held on: February 22nd, 2024 from 2-3 PM ET

Open Science↗
DOE OSTI2020

dCache - Keeping up With the Evolution of Science

The dCache project provides open-source software deployed internationally to satisfy ever more demanding storage requirements of various scientific communities. Its multifaceted approach provides an integrated way of supporting different use-cases with the same storage, from high throughput data ingest, through wide access and easy integration with existing systems, including event driven workflow management. With this presentation, we will show some of the recent developments that optimize data management and access to maximise the gain from stored data.

Mkrtchyan, Tigran↗
DOE OSTI2022

ORNL Second Target Station Project: Biological & Environmental Science Workshop

Recent advances in neutron sources and instrumentation have opened up many new opportunities for the application of neutron scattering techniques in the biological and environmental sciences. Neutrons enable studies of the structure and dynamics of biological and environmental samples with a particular sensitivity to light elements, such as hydrogen, which is a key component of biological and environmental samples. Studies using neutrons are complementary to X-rays and have the unique advantage of being non-destructive and highly-penetrating. Oak Ridge National Laboratory’s upcoming Spallation Neutron Source (SNS) Second Target Station (STS) will provide high brightness cold neutron sources that significantly advance the scientific capabilities of neutron scattering instruments. The STS will advance our understanding of biological and environmental processes across spatial and temporal scales. The capabilities will enhance our ability to discover, design, and develop new materials essential for advanced sustainable technologies to address society’s most pressing needs. This report summarizes the discussions and recommendations from a joint workshop held by the STS Project and the Biological and Environmental Systems Science Directorate (BESSD) in June 2022. The purpose of the workshop was to explore science opportunities and capabilities related to biological and environmental systems that could be incorporated into both current and future STS instrument designs, as well as additional instruments at the SNS First Target Station (FTS) and High Flux Isotope Reactor (HFIR). With six breakout sessions, each with two invited plenary speakers from other institutions, the participants discussed a wide range of topics relevant to biological and environmental research. Based on the input from participants, a number of recommendations on instrumentation, sample environments, complementary multi-modal methods, data processing and analysis and sample deuteration are provided in the report. The participants also identified science opportunities that are emerging from the planned instrument capabilities at STS. Selected recommendations and science opportunities are listed in the Executive Summary.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

gRASPA

GPU Monte Carlo Simulation Code with a taste of RASPA We present enhancements in Monte Carlo simulation speed and functionality within an open-source code, gRASPA, which uses graphical processing units (GPUs) to achieve significant performance improvements compared to serial, CPU implementations of Monte Carlo. The code supports a wide range of Monte Carlo simulations, including canonical ensemble (NVT), grand canonical, NVT Gibbs, Widom test particle insertions, and continuous-fractional component Monte Carlo. Implementation of grand canonical transition matrix Monte Carlo (GC-TMMC) and a novel feature to allow different moves for the different components of metal-organic framework (MOF) structures exemplify the capabilities of gRASPA for precise free energy calculations and enhanced adsorption studies, respectively. The introduction of a High-Throughput Computing (HTC) mode permits many Monte Carlo simulations on a single GPU device for accelerated materials discovery. The code can incorporate machine learning (ML) potentials. The open-source nature of gRASPA promotes reproducibility and openness in science, and users may add features to the code and optimize it for their own purposes. The code is written in CUDA/C++ and SYCL/C++ to support different GPU vendors. The gRASPA code is publicly available at https://github.com/snurr-group/gRASPA.

Li, Zhao [Purdue/Northwestern/Notre Dame Universit↗
DOE OSTI2022

CASM — A software package for first-principles based study of multicomponent crystalline solids

CASM is a software package that enables first-principles based studies of crystalline materials. It has been designed to treat coupled chemical, mechanical, vibrational, and magnetic degrees of freedom to determine ground state and finite temperature properties of crystals. The symmetry of the underlying parent crystal structure is used to enumerate perturbations of the parent crystal structure and generate derivative structures which can be input to first-principles calculations in order to explore the ground state energy landscape. CASM algorithmically constructs cluster expansions that fully couple discrete and continuous degrees of freedom, and generates highly efficient code to evaluate the cluster expansion basis functions. Widely used machine learning methods are integrated for fitting expansion coefficients to first-principle calculations. The fully parameterized cluster expansions can be combined with (kinetic) Monte Carlo methods to calculate finite temperature thermodynamic and kinetic properties. CASM Alloy Manager identifies distinct parent crystal structures in an alloy system, creating individual projects for each, and integrating the results. The integrated infrastructure facilitates the linkage between first-principles statistical mechanics predictions with higher length scale computational methods, such as phase field simulations. In conclusion, CASM projects are designed to be easy to share in repositories, to re-use, and to extend to include additional chemical species or types of degrees of freedom.

36 MATERIALS SCIENCE↗
DOE OSTI2023

GeoThermalCloud: A Machine Learning Tool for Discovery, Exploration, and Development of Hidden Geothermal Resources

In this 25 minute presentation, we showcase our open source “GeoThermalCloud” tool for identifying hidden geothermal resources using a publicly available dataset for southwestern New Mexico. The presenters include Bulbul Ahmmed and Luke Frash. All of the visuals use source material from LA-UR approved publications and this work falls under the Earth Sciences DUSA. The code shown in this video is already released with LANL approval in open source format on GitHub and DockerHub. The audio in this video includes only material on the topics of geothermal energy and machine learning applied to geothermal energy. The primary machine learning method used is LANL’s Non-negative Matrix Factorization “NMFk” method. Modeling work also mentions LANL’s Geothermal Design Tool “GeoDT” which is another approved open source code that has been released by LANL. This work was performed for DOE Geothermal Technologies Office (DE-EE-3.1.8.1). The host for the released video is intended to be YouTube or a suitable perpetual data repository such as GDR.

15 GEOTHERMAL ENERGY↗
DOE OSTI2020

The MolSSI QCArchive project: An open-source platform to compute, organize, and share quantum chemistry data

The Molecular Sciences Software Institute's (MolSSI) Quantum Chemistry Archive (QCArchive) project is an umbrella name that covers both a central server hosted by MolSSI for community data and the Python-based software infrastructure that powers automated computation and storage of quantum chemistry (QC) results. The MolSSI-hosted central server provides the computational molecular sciences community a location to freely access tens of millions of QC computations for machine learning, methodology assessment, force-field fitting, and more through a Python interface. Facile, user-friendly mining of the centrally archived quantum chemical data also can be achieved through web applications found at the website. The software infrastructure can be used as a standalone platform to compute, structure, and distribute hundreds of millions of QC computations for individuals or groups of researchers at any scale. The QCArchiveInfrastructure is open-source (BSD-3C), code repositories can be found at github, and releases can be downloaded via PyPI and Conda. This article is categorized under: Electronic Structure Theory > Ab Initio Electronic Structure Methods Software > Quantum Chemistry Data Science > Computer Algorithms and Programming

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

Advanced data science toolkit for non-data scientists – A user guide

Emerging modern data analytics attracts much attention in materials research and shows great potential for enabling data-driven design. Data populated from the high-throughput CALPHAD approach enables researchers to better understand underlying mechanisms and to facilitate novel hypotheses generation, but the increasing volume of data makes the analysis extremely challenging. Here in this paper, we introduce an easy-to-use, versatile, and open-source data analytics frontend, ASCENDS (Advanced data SCiENce toolkit for Non-Data Scientists), designed with the intent of accelerating data-driven materials research and development. The toolkit is also of value beyond materials science as it can analyze the correlation between input features and target values, train machine learning models, and make predictions from the trained surrogate models of any scientific dataset. Various algorithms implemented in ASCENDS allow users performing quantified correlation analyses and supervised machine learning to explore any datasets of interest without extensive computing and data science background. The detailed usage of ASCENDS is introduced with an example of experimental high-temperature alloy data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

Efficient Data Management in Neutron Scattering Data Reduction Workflows at ORNL

Oak Ridge National Laboratory (ORNL) experimental neutron science facilities produce 1.2 TB a day of raw event-based data that is stored using the standard metadata-rich NeXus schema built on top of the HDF5 file format. Performance of several data reduction workflows is largely determined by the amount of time spent on the loading and processing algorithms in Mantid, an open-source data analysis framework used across several neutron sciences facilities around the world. The present work introduces new data management algorithms to address identified input output (I/O) bottlenecks on Mantid. First, we introduce an in-memory binary-tree metadata index that resemble NeXus data access patterns to provide a scalable search and extraction mechanism. Second, data encapsulation in Mantid algorithms is optimally redesigned to reduce the total compute and memory runtime footprint associated with metadata I/O reconstruction tasks. Results from this work show speed ups in wall-clock time on ORNL data reduction workflows, ranging from 11% to 30% depending on the complexity of the targeted instrument-specific data. Nevertheless, we highlight the need for more research to address reduction challenges as experimental data volumes increase.

Godoy, William↗
DOE OSTI2021

A validated thermal and biological model for predicting algal productivity in large scale outdoor cultivation systems

Proper assessment of the sustainability of algal products requires an understanding of algal growth rate with regional and temporal resolution. This study developed a bulk growth model that utilizes geospatial data, reactor geometry inputs, and a maximum of six strain-specific parameters to predict algal productivity with temporal and regional resolution. The model requires local weather data to calculate the time-resolved culture temperature which is combined with a biological model based on maximum theoretical carbon fixation to predict the time-resolved algal concentration. Validation of the model against experimental data illustrates an average ac- curacy of 0.27% ± 5.32% when comparing algal concentration at harvest over 45 days of cultivation data for Galdieria sulphuraria 5587.1, and Galdieria sulphuraria Soos grown in photobioreactors (PBRs) in Mesa, AZ (33.4152° N, 111.8315° W). For open raceway ponds (ORPs), an accuracy of 0.9% ± 2.35% was achieved when comparing algal concentration at harvest over 50 days of cultivation data for Chlorella vulgaris, Desmodesmus intermedius, Nannochloropsis oceanica grown in Atlanta, GA (33.7490° N, 84.3880° W) and Mesa, AZ (33.4152° N, 111.8315° W). The validation trials spanned three seasons (winter, spring, and summer) to ensure model ac- curacy throughout the year, and concentration between harvests was modeled with hourly resolution. The validated model is used to assess the productivity at several case-study locations in the United States with Chlorella vulgaris, grown in open raceway ponds and Galdieria sulphuraria grown in vertical flat panel photo- bioreactors. In conclusion, the model has been published as an open-source tool, in an effort to increase the fidelity of future studies that rely on outdoor algae cultivation for feedstock production and allow for time resolved results for future techno-economic and life cycle assessments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Short-term electricity load forecasting: Application-driven evaluation of machine learning models across spatial and temporal scales

As we transition towards a decarbonized economy, the integration of variable renewable energy resources and new demands (e.g., electric vehicles, heat pumps) into the electricity grid places unprecedented pressure on grid operators to effectively anticipate and manage peak load. In this context, machine learning algorithms are proving to be indispensable for accurate short-term load forecasting, a crucial task to address these challenges. This study benchmarks 6 machine learning algorithms, including three neural networks and three tree-based algorithms, across various levels of spatial aggregation and time horizons (1, 4, 8, 24, and 48 h). The central contribution of this work is the comparison and analysis of load forecasting models not only based on statistical metrics, but also based on a novel error metric, which evaluates the cost implications of forecast errors for power system stakeholders. Results show that tree-based models outperform neural networks, based on statistical metrics, and yield less skewed error distributions for most spatial scales. However, through the lens of the novel error metric, neural networks are the more competitive choice, especially for forecast horizons that exceed 8 h. The study concludes with actionable recommendations to grid operators and highlights the need for the development of error metrics that link forecasting accuracy to operational costs. To promote transparency and open science, the datasets and Python code are open-sourced via a supplementary repository.

Houben, Nikolaus↗
DOE OSTI2024

Attosecond x-ray free-electron lasers utilizing an optical undulator in a self-selection regime

Accelerator-based x-ray free-electron lasers (XFELs) are the latest addition to the revolutionary tools of discovery for the 21st century. The two major components of an XFEL are an accelerator-produced electron beam and a magnetic undulator, which tend to be kilometer-scale long and expensive. A proof-of-principle demonstration of free-electron lasing at 27 nm using beams from compact laser wakefield accelerators was shown recently by using a magnetic undulator. However, scaling these concepts to x-ray wavelengths is far from straightforward as the requirements on the beam quality and jitters become much more stringent. Here, we present an ultracompact scheme to produce tens of attosecond x-ray pulses with several GW peak power utilizing a novel aspect of the FEL instability using a highly chirped, prebunched, and ultrabright tens of MeV electron beam from a plasma-based accelerator interacting with an optical undulator. The FEL resonant relation between the prebunched period and the energy selects resonant electrons automatically from the highly chirped beam which leads to a stable generation of attosecond x-ray pulses. Furthermore, two-color attosecond pulses with subfemtosecond separation can be produced by adjusting the energy distribution of the electron beam so that multiple FEL resonances occur at different locations within the beam. Such a tunable coherent attosecond x-ray sources may open up a new area of attosecond science enabled by x-ray attosecond pump/probe techniques. Published by the American Physical Society 2024

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Three-dimensional modeling of hyphal fusion, branching, and nutrient transport in filamentous fungi

Fungi exhibit behaviors distinct from other microbes. Filamentous fungi grow by extending complex networks of branched filaments collectively referred to as the mycelium. These networks can expand over large distances and traverse low-nutrient areas by translocating nutrients through the filament network. This spatial characteristic makes filamentous fungi crucial for soil ecosystems, supporting stable microbial communities and promoting plant growth. However, simulating these behaviors is complex. The elongated nature of fungal compartments results in different mechanical interactions compared to the commonly modeled spherical bacteria. These detailed hyphal mechanics require specialized consideration and are often excluded from conventional fungal simulation packages. Additionally, the extensive fungal networks in nature demand computationally intensive simulations, necessitating high-performance algorithms. Therefore, realistic fungi simulations require specialized software. Here, we introduce a fungal modeling expansion to the high-performance biological modelling and interface exchange (bmx) software suite. bmx leverages adaptive mesh refinement in AMReX for chemical diffusion and incorporates a full mechanical model for bacterial cells, accelerated by GPUs. By extending bmx to model filamentous particles, we demonstrate the formation of complex filament networks through interactions like hyphal branching and fusion (anastomosis). We show that the networks produced match real-world fungal structures through various metrics. This work supports computational studies of fungal growth dynamics and can be adapted to investigate the growth of other filamentous structures in biology or materials science. The expanded-BMX package is open-sourced and is available online.

Cell mechanics↗
DOE OSTI2021

Efficient loading of reduced data ensembles produced at ORNL SNS/HFIR neutron time-of-flight facilities

We present algorithmic improvements to the loading operations of certain reduced data ensembles produced from neutron scattering experiments at Oak Ridge National Laboratory (ORNL) facilities. Ensembles from multiple measurements are required to cover a wide range of the phase space of a sample material of interest. They are stored using the standard NeXus schema on individual HDF5 files. This makes it a scalability challenge, as the number of experiments stored increases in a single ensemble file. The present work follows up on our previous efforts on data management algorithms, to address identified input output (I/O) bottlenecks in Mantid, an open-source data analysis framework used across several neutron science facilities around the world. We reuse an in-memory binary-tree metadata index that resembles data access patterns, to provide a scalable search and extraction mechanism. In addition, several memory operations are refactored and optimized for the current common use cases, ranging most frequently from 10 to 180, and up to 360 separate measurement configurations. Results from this work show consistent speed ups in wall-clock time on the Mantid LoadMD routine, ranging from 19% to 23% on average, on ORNL production computing systems. The latter depends on the complexity of the targeted instrument-specific data and the system I/O and compute variability for the shared computational resources available to users of ORNL’s Spallation Neutron Source (SNS) and the High Flux Isotope Reactor (HFIR) instruments. Nevertheless, we continue to highlight the need for more research to address reduction challenges as experimental data volumes, user time and processing costs increase.

Godoy, William↗
DOE OSTI2021

Kinetic Monte Carlo for semiconductor surface science v.1

SAND2020-14229 O This software builds on an open source Kinetic Monte Carlo library to evaluate the incorporation rate of semiconductor dopant atoms in an Atomic Precision Advanced Manufacturing (APAM) process. 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

Campbell, Quinn↗
DOE OSTI2024

Database schema design: Energy Flexibility Environmental Tradeoffs Tool

The Energy Flexibility-Environment Tradeoff Toolset is designed using Streamlit framework, with Python as the programming language. Data storage is facilitated through the use of SQLite. Streamlit is an open-source Python framework for machine learning and data science teams, and SQLite is the most used database engine.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2020

WHONDRS-GUI: a web application for global survey of surface water metabolites

Background The Worldwide Hydrobiogeochemistry Observation Network for Dynamic River Systems (WHONDRS) is a consortium that aims to understand complex hydrologic, biogeochemical, and microbial connections within river corridors experiencing perturbations such as dam operations, floods, and droughts. For one ongoing WHONDRS sampling campaign, surface water metabolite and microbiome samples are collected through a global survey to generate knowledge across diverse river corridors. Metabolomics analysis and a suite of geochemical analyses have been performed for collected samples through the Environmental Molecular Sciences Laboratory (EMSL). The obtained knowledge and data package inform mechanistic and data-driven models to enhance predictions of outcomes of hydrologic perturbations and watershed function, one of the most critical components in model-data integration. To support efforts of the multi-domain integration and make the ever-growing data package more accessible for researchers across the world, a Shiny/R Graphical User Interface (GUI) called WHONDRS-GUI was created. Results The web application can be run on any modern web browser without any programming or operational system requirements, thus providing an open, well-structured, discoverable dataset for WHONDRS. Together with a context-aware dynamic user interface, the WHONDRS-GUI has functionality for searching, compiling, integrating, visualizing and exporting different data types that can easily be used by the community. The web application and data package are available at https://data.ess-dive.lbl.gov/view/doi:10.15485/1484811 , which enables users to simultaneously obtain access to the data and code and to subsequently run the web app locally. The WHONDRS-GUI is also available for online use at Shiny Server ( https://xmlin.shinyapps.io/whondrs/ ).

59 BASIC BIOLOGICAL SCIENCES↗