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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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DOE OSTI2020

JPype

JPype is a Python module to provide full access to Java from within Python. It allows Python to make use of Java only libraries, exploring and visualization of Java structures, development and testing of Java libraries, scientific computing, and much more. By gaining the best of both worlds using Python for rapid prototyping and Java for strong typed production code, JPype provides a powerful environment for engineering and code development. This is achieved not through re-implementing Python, as Jython has done, but rather through interfacing at the native level in both virtual machines. This shared memory based approach achieves decent computing performance, while providing the access to the entirety of CPython and Java libraries.

Nelson, KarlE.↗
DOE OSTI2021

SMC 2021 : Analyzing Resource Utilization and User Behavior on Titan Supercomputer

Resource utilization statistics of submitted jobs on a supercomputer can help us understand how users from various scientific domains use HPC platforms and better design a job scheduler. We explore to generate insight regarding workload distribution and usage pattern domains from job scheduler trace, GPU failure information, and project-specific information collected from Titan supercomputer. Furthermore, we want to know how the scheduler performance varies over time and how the users' scheduling behavior changes following a system failure. These observations have the potential to provide valuable insight, which is helpful to prepare for system failures. These practices will help us develop and apply novel machine learning algorithms in understanding system behavior, requirement, and better scheduling of HPC systems. There are two datasets, RUR and GPU. RUR: This dataset is the job scheduler traces collected from the Titan supercomputerfrom 01/01/2015 to 07/31/2019 (2015.csv - 2019.csv). These were collected usingResource Utilization Report (RUR), a Cray-developed resource-usage data collectionand reporting system. It contains the usage information of its critical resources (CPU,Memory, GPU, and I/O) of each running job on Titan during that period [2]. ProjectAreas: Every job is associated with a project ID. TheProjectAreas.csvdatasetprovides a mapping of the project ID to its domain science. GPU: There have been some hardware-related issues in the GPUs in Titan that caused some GPUs to fail, sometimes irrecoverably during some job runs. This dataset provides information regarding these failures during the execution of the submitted jobs. GPUs on Titan are uniquely identified by a serial number (SN), and they are installed in a location. A GPU can be installed in a location, then removed from that location following a failure, and then re-installed in a different location after fixing the problem. If the failure can't be recovered, the GPU might be removed entirely from Titan. There are two prominent types of failures that resulted in the removal of GPUs from Titan: Double Bit Error (DBE) and Out of the Bus (OTB). The dataset (gc_full.csv) has the following fields: 1. SN : Serial number of a GPU 2. location : The location where it is installed 3. insert : The time when it was inserted into that location 4. remove : The time when it was removed from that location 5. duration : Amount of time the GPU spent in this location 6. out : If the device was taken out entirely w/o a re-installment into a new location. 7. event : If the GPU was taken out entirely, the reason for its removal. To learn more about this dataset, please refer to the git repositoryhttps://github.com/olcf/TitanGPULifeand the related publication [1]. References [1] George Ostrouchov, Don Maxwell, Rizwan A Ashraf, Christian Engelmann, MallikarjunShankar, and James H Rogers. Gpu lifetimes on titan supercomputer: Survival analysisand reliability. InSC20: International Conference for High Performance Computing,Networking, Storage and Analysis, pages 1-14. IEEE, 2020. [2] Feiyi Wang, Sarp Oral, Satyabrata Sen, and Neena Imam. Learning from five-yearresource-utilization data of titan system. In2019 IEEE International Conference onCluster Computing (CLUSTER), pages 1-6. IEEE, 2019.

42 ENGINEERING↗
DOE OSTI2021

SMC 2021 Data Challenge: Analyzing Resource Utilization and User Behavior on Titan Supercomputer

Resource utilization statistics of submitted jobs on a supercomputer can help us understand how users from various scientific domains use HPC platforms and better design a job scheduler. We explore to generate insight regarding workload distribution and usage pattern domains from job scheduler trace, GPU failure information, and project-specific information collected from Titan supercomputer. Furthermore, we want to know how the scheduler performance varies over time and how the users' scheduling behavior changes following a system failure. These observations have the potential to provide valuable insight, which is helpful to prepare for system failures. These practices will help us develop and apply novel machine learning algorithms in understanding system behavior, requirement, and better scheduling of HPC systems. There are two datasets, RUR and GPU: RUR dataset is the job scheduler traces collected from the Titan supercomputer from 01/01/2015 to 07/31/2019 (2015.csv - 2019.csv). These were collected using resource Utilization Report (RUR), a Cray-developed resource-usage data collection and reporting system. It contains the usage information of its critical resources (CPU, Memory, GPU, and I/O) of each running job on Titan during that period (https://ieeexplore.ieee.org/abstract/document/8891001). It includes ProjectAreas as additional information, every job is associated with a project ID. TheProjectAreas.csv dataset provides a mapping of the project ID to its domain science. GPU dataset has information regarding GPU failure on Titan. There have been some hardware-related issues in the GPUs in Titan that caused some GPUs to fail, sometimes irrecoverably during some job runs. This dataset provides information regarding these failures during the execution of the submitted jobs. GPUs on Titan are uniquely identified by a serial number (SN), and they are installed in a location. A GPU can be installed in a location, then removed from that location following a failure, and then re-installed in a different location after fixing the problem. If the failure can't be recovered, the GPU might be removed entirely from Titan. There are two prominent types of failures that resulted in the removal of GPUs from Titan: Double Bit Error (DBE) and Out of the Bus (OTB). The dataset (gc_full.csv) has seven attributes, we provided a short description of these attributes in the ReadMe file. To learn more about this dataset, please refer to the git repository https://github.com/olcf/TitanGPULife and the related publication (https://ieeexplore.ieee.org/abstract/document/9355319).

42 ENGINEERING↗
DOE OSTI2020

UC LANL Postdoc Entrepreneur Fellowship Cohort Four

Three Laboratory postdoctoral researchers have been selected for this year's cohort of Entrepreneur Fellows. The six-month fellowship program - a joint initiative of the University of California and Los Alamos, now in its fourth year - supports early career scientists explore applications and evaluate market opportunities for their technologies. The Lab selects Entrepreneur Fellows based on the potential for their projects to have both scientific and commercial impact. The fellows receive six months of facilitated training and mentoring, as well as funding to engage in customer discovery, market exploration and technology advancement

36 MATERIALS SCIENCE↗
DOE OSTI2022

A Strategy for NACS investment in Machine Learning

The Nuclear and Chemical Sciences (NACS) Division furnishes the expertise in the scientific areas of chemical, nuclear and isotopic sciences that are foundational in the Laboratory’s national security missions. This expertise is maintained and advanced through identification, development and application of state-of-the-art theoretical, computational and experimental methods and tools. Recent developments in artificial intelligence and machine learning (AI/ML) techniques enabled by advances in computing capabilities and widespread availability of powerful software implementations have made use of these techniques ubiquitous across both science and industry. While the scope of AI/ML applications is incredibly large and evolves very rapidly, the topics most relevant to NACS missions fall into the general category of detecting, categorizing or identifying features in large, complex datasets using either supervised or unsupervised learning. This covers both basic scientific data analysis and the development of efficient surrogate models of real-life technological systems, experimental detectors, or theoretical models. To remain at the forefront of its core scientific disciplines, NACS must both cultivate ML expertise as well as continuously explore applying this expertise to new problems or utilizing new methods. This document identifies the key areas where this support is critical and provides a strategy for investing in them.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2021

Leveraging In-Network Computing and Programmable Switches for Streaming Analysis of Scientific Data

With the emergence of programmable network devices that match the performance of fixed function devices, several recent projects have explored in-network computing, where the processing that is traditionally done outside the network is offloaded to the network devices. In-network computing has typically been applied to network functions (e.g., load balancing, NAT, and DNS), caching, data reduction/aggregation, and coordination/consensus functions. In some cases it has been used to accelerate stream-processing tasks that involve small payloads and simple operations. In this work we focus on leveraging in-network computing for stream processing of scientific datasets with large payloads that require complex operations such as floating-point computations and logarithmic functions. We demonstrate in-network computing for a real-world scientific application performing streaming normalization of a 2-D image from a light source experiment. We discuss the challenges we encountered and potential approaches to address them.

Sankaran, Ganesh↗
DOE OSTI2020

OSTI Semantic Thesaurus v1

The OSTI Semantic Thesaurus is a reference for scientific and technical terms and relationships. The term data is used to implement keyword-to-concept mapping in OSTI.GOV searches, where searched term(s) will be mapped to related scientific concepts, allowing the end user to retrieve results related to their search terms and also explore narrower and similar concepts. The OSTI Semantic Thesaurus originally inherited data and structure from the INIS/ETDE Thesaurus (https://www.etde.org/edb/IAEA-INIS-ETDE-01-2013-08.pdf), which was a controlled terminology for indexing information within the subject scopes of the International Nuclear Information System (INIS) and the Energy Technology Data Exchange (ETDE). The data has been expanded upon since that time with the inclusion of scientific concepts from sources like Wikidata, and through manual curation. This dataset is an export from the OSTI Semantic Thesaurus in RDF/SKOS format, which is specifically suited for representation of controlled vocabularies like thesauri. While the full thesaurus includes additional relation types which may be included in future revisions, this export is limited to broader, narrower, and related term relations. Definitions and scope notes are included where available.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

A restructured and updated global soil respiration database (SRDB-V5)

Field-measured soil respiration (R S , the soil-to-atmosphere CO 2 flux) observations were compiled into a global soil respiration database (SRDB) a decade ago, a resource that has been widely used by the biogeochemistry community to advance our understanding of R S dynamics. Novel carbon cycle science questions require updated and augmented global information with better interoperability among datasets. Here, we restructured and updated the global R S database to version SRDB-V5. The updated version has all previous fields revised for consistency and simplicity, and it has several new fields to include ancillary information (e.g., R S measurement time, collar insertion depth, collar area). The new SRDB-V5 includes published papers through 2017 (800 independent studies), where total observations increased from 6633 in SRDB-V4 to 10 366 in SRDB-V5. The SRDB-V5 features more R S data published in the Russian and Chinese scientific literature and has an improved global spatio-temporal coverage and improved global climate space representation. We also restructured the database so that it has stronger interoperability with other datasets related to carbon cycle science. For instance, linking SRDB-V5 with an hourly timescale global soil respiration database (HGRsD) and a community database for continuous soil respiration (COSORE) enables researchers to explore new questions. The updated SRDB-V5 aims to be a data framework for the scientific community to share seasonal to annual field R S measurements, and it provides opportunities for the biogeochemistry community to better understand the spatial and temporal variability in R S , its components, and the overall carbon cycle.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Stimulated geologic hydrogen: from mechanistic control to engineered rock transformation

Geologic hydrogen (GeoH 2 ) generated from subsurface iron-rich rock–water reaction (i.e., serpentinization) is emerging as a promising candidate for the next primary energy source. Yet, accelerating GeoH 2 production from geological to human timescales via enhanced serpentinization remains a formidable scientific and technical challenge. Here, in this Review, we decipher the mechanistic control and explore strategies for accelerating in situ, engineered iron-rich rock transformation into carbon-free GeoH 2 by orders of magnitude. Serpentinization rate is hindered by low porosity and permeability of source rocks, suboptimal temperatures, unfavorable water chemistry, inefficient Fe 2+ -to-Fe 3+ conversion, thermodynamic constraints, and low reactive surface area. While closed-system experiments provide valuable mechanistic insights, open-system conditions with fluid circulation are more crucial for economically viable GeoH 2 production. We assess stimulation techniques from enhanced hydrocarbon and geothermal recovery as tools to be adopted or adapted for increasing reactive surface areas for stimulated GeoH2 production. We estimate that 7.40 × 10 5 to 1.73 × 10 6 million metric tons (Mt) of hydrogen could be engineered over 20 to 50 years from about 10% iron-rich rocks within 10 km depth of continental crust. Enabling GeoH 2 as a viable energy source requires not only advancing scientific frontiers but also forming a global GeoH 2 research network and innovation ecosystem to address the critical scientific, technical, societal, economic, and policy challenges.

08 HYDROGEN↗
DOE OSTI2024

Cities Are Concentrators of Complex, MultiSectoral Interactions Within the Human-Earth System

Cities are concentrators of complex, multi-sectoral interactions. As keystones in the interconnected human-Earth system, cities have an outsized impact on the Earth system. We describe a multi-lens framework for organizing our understanding of the complexity of urban systems and scientific research on urban systems, which may be useful for natural system scientists exploring the ways their work can be made more actionable. We then describe four critical dimensions along which improvements are needed to advance the urban research that addresses urgent climate challenges: (a) solutions-oriented research, (b) equity-centered assessments which rely on fine-scale human and ecological data, (c) co-production of knowledge, and (d) better integration of human and natural systems occurring through theory, observation, and modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

MSD CoP Webinar: Accounting for distributive justice in model-based decision support

Context: This webinar was hosted by the MultiSector Dynamics Community of Practice (MSD CoP; https://multisectordynamics.org). Abstract: Model-based analyses play an ever-increasing role in informing policy and decision-making. However, many large-scale societal challenges unavoidably involve questions about the fair distribution of positive and negative consequences. How are the costs of the energy transition distributed over households and businesses? How are flood risks redistributed under different flood risk management plans? How are the impacts of climate change and climate mitigation distributed over different parts of the world?These kinds of questions play an important role when deliberating public policy, and the lack, in many cases, of clear answers becomes an obstacle to decision-making. At recent COPs, for example, wicked questions about loss and damages and the phase-out versus phase-down of coal became obstacles to global climate action, illustrating the misalignment of the direction of scientific research and decision-makers' information needs. In this talk, I'll explore advances that are being made that enable analysts to start providing grounded model-based answers to questions of distributive justice, as well as argue that analysts should embrace and explicate the normative nature of their work instead of hiding behind the purported neutrality of science. Presenters: Dr. Jan Kwakkel (Delft University of Technology) Moderator(s): Rebecca Saari (MSD CoP Working Group Co-Chair; Univ. of Waterloo), Matt Sparks (Univ. of Waterloo); Sarah Fletcher (MSD CoP Working Group Co-Chair; Stanford University), Juan Moreno-Cruz (Univ. of Waterloo), Patrick Reed (MSD CoP Facilitation Team Member, Moderator and Organizer) This webinar was held on: November 13, 2024 from 1 PM - 2:15 PM ET

Kwakkel, Jan H.↗
DOE OSTI2023

Computational exploration of DIII-D QH-mode with extended MHD

This work will enable improved understanding of the scientific basis for DIII-D Quiescent H-mode (QH-mode) by leveraging recent computational developments of the NIMROD code. Experimentally, it is established that the QH-mode tokamak operation regime is free of edge-localized modes (ELMs) with good energy confinement. ELMs produce an impulsive heat-flux on the divertor that is unacceptable under burning-plasma conditions. Beyond DIII-D in future high-power-density tokamaks, accommodation of a high heat flux at the divertor represents a key challenge to overcome. Proposed solutions involve operation at high divertor density to achieve divertor detachment, operation with a liquid lithium wall and/or introduction of impurities to enhance radiative losses. Additionally, the first wall material is likely to be a high-Z material such as Tungsten in future devices as materials such as graphite tiles restrict the lifetime of the first wall due to a high erosion rate. While the atomic species composition of the plasma is known to impact the edge-pedestal structure and stability, the specifics of the underlying physics basis for this interaction is not firm. Studying this interaction is the goal of this proposal without focusing specifically on the fine details of the first wall and divertor solution. It is the goal is to lay the ground work to establish the compatibility of the divertor solutions and wall material with pedestal solutions that eliminate ELMs by leveraging present DIII-D experiments.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Scripting a new dialogue between diazotrophs and crops

Diazotrophs are bacteria and archaea that can reduce atmospheric dinitrogen (N 2 ) into ammonium. Plant–diazotroph interactions have been explored for over a century as a nitrogen (N) source for crops to improve agricultural productivity and sustainability. This scientific quest has generated much information about the molecular mechanisms underlying the function, assembly, and regulation of nitrogenase, ammonium assimilation, and plant–diazotroph interactions. This review presents various approaches to manipulating N fixation activity, ammonium release by diazotrophs, and plant–diazotroph interactions. Here, we discuss the research avenues explored in this area, propose potential future routes, emphasizing engineering at the metabolic level via biorthogonal signaling, and conclude by highlighting the importance of biocontrol measures and public acceptance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Position Papers for the ASCR Workshop on the Science of Scientific-Software Development and Use

Software is an increasingly important component in the pursuit of scientific discovery. Both its development and use are essential activities for many scientific teams. At the same time, very little scientific study has been conducted to understand, characterize, and improve the development and use of software for science. Computational science teams have diversified over time to include contributions from domain scientists who provide expertise in scientific and engineering disciplines, applied mathematicians and computer scientists who provide optimal algorithms and data structures, and software and data engineers who provide methodologies and tools adapted and adopted from other software domains. These diverse contributions have enabled tremendous advances in the pursuit of scientific discovery, even as models, computer architectures, and software environments have become more complicated. With this increasing diversity, we believe the next opportunity for qualitative improvement comes from applying the scientific method to understanding, characterizing, and improving how scientific software is developed and used. We believe that this pursuit requires expertise from computational scientists themselves, and from the cognitive and social sciences as well as the software engineering research community. As we look to increase the productivity and sustainability of the scientific-software-development-and-use cycle, a more systematic application of the scientific method to understand processes for software development and use will be a valuable tool to guide future work and result in more usable and sustainable software. This workshop will bring together computer scientists, software engineering researchers, computational scientists, applied mathematicians, social scientists, cognitive scientists, and others, to explore how we can conduct such systematic investigations, what can be learned, and how doing so will benefit the scientific enterprise. The workshop will be structured around a set of breakout sessions, with every attendee expected to participate actively in the discussions. Afterward, workshop attendees — from DOE, industry, and academia — will produce a report for ASCR that summarizes the findings of the workshop.

42 ENGINEERING↗
DOE OSTI2020

From smart homes to smart laboratories: connected instruments for materials science

The current focus on artificial intelligence and machine learning in the scientific community has the potential to greatly speed up discovery. In this article, we explore what a “smart facility” would mean for materials science. We propose to capture meta-data at every step of an experiment, including materials synthesis, sample production and characterization, simulation, and the analysis software used to extract information. Although most of this information is captured in various institutional systems and staff logbooks, more insight could be obtained by connecting this information through a system that allows automation. AI-enabled processes built on such a system would have the potential of making experiment planning easier and minimize the time between experiment and publication.

Doucet, Mathieu↗
DOE OSTI2022

Expanding the Biological Role of Lipo-Chitooligosaccharides and Chitooligosaccharides in Laccaria bicolor Growth and Development

The role of lipo-chitooligosaccharides (LCOs) as signaling molecules that mediate the establishment of symbiotic relationships between fungi and plants is being redefined. New evidence suggests that the production of these molecular signals may be more of a common trait in fungi than what was previously thought. LCOs affect different aspects of growth and development in fungi. For the ectomycorrhizal forming fungi, Laccaria bicolor, the production and effects of LCOs have always been studied with a symbiotic plant partner; however, there is still no scientific evidence describing the effects that these molecules have on this organism. Here, we explored the physiological, molecular, and metabolomic changes in L. bicolor when grown in the presence of exogenous sulfated and non-sulfated LCOs, as well as the chitooligomers, chitotetraose (CO 4 ), and chitooctaose (CO8). Physiological data from 21 days post-induction showed reduced fungal growth in response to CO and LCO treatments compared to solvent controls. The underlying molecular changes were interrogated by proteomics, which revealed substantial alterations to biological processes related to growth and development. Moreover, metabolite data showed that LCOs and COs caused a downregulation of organic acids, sugars, and fatty acids. At the same time, exposure to LCOs resulted in the overproduction of lactic acid in L. bicolor. Altogether, these results suggest that these signals might be fungistatic compounds and contribute to current research efforts investigating the emerging impacts of these molecules on fungal growth and development.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Eight Up-Coming Biotech Tools to Combat Climate Crisis

Biotechnology has a high potential to substantially contribute to a low-carbon society. Several green processes are already well established, utilizing the unique capacity of living cells or their instruments. Beyond that, the authors believe that there are new biotechnological procedures in the pipeline which have the momentum to add to this ongoing change in our economy. Eight promising biotechnology tools were selected by the authors as potentially impactful game changers: (i) the Wood–Ljungdahl pathway, (ii) carbonic anhydrase, (iii) cutinase, (iv) methanogens, (v) electro-microbiology, (vi) hydrogenase, (vii) cellulosome and, (viii) nitrogenase. Some of them are fairly new and are explored predominantly in science labs. Others have been around for decades, however, with new scientific groundwork that may rigorously expand their roles. In the current paper, the authors summarize the latest state of research on these eight selected tools and the status of their practical implementation. We bring forward our arguments on why we consider these processes real game changers.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

32 examples of LLM applications in materials science and chemistry: towards automation, assistants, agents, and accelerated scientific discovery

Abstract Large language models (LLMs) are reshaping many aspects of materials science and chemistry research, enabling advances in molecular property prediction, materials design, scientific automation, knowledge extraction, and more. Recent developments demonstrate that the latest class of models are able to integrate structured and unstructured data, assist in hypothesis generation, and streamline research workflows. To explore the frontier of LLM capabilities across the research lifecycle, we review applications of LLMs through 32 total projects developed during the second annual LLM hackathon for applications in materials science and chemistry, a global hybrid event. These projects spanned seven key research areas: (1) molecular and material property prediction, (2) molecular and material design, (3) automation and novel interfaces, (4) scientific communication and education, (5) research data management and automation, (6) hypothesis generation and evaluation, and (7) knowledge extraction and reasoning from the scientific literature. Collectively, these applications illustrate how LLMs serve as versatile predictive models, platforms for rapid prototyping of domain-specific tools, and much more. In particular, improvements in both open source and proprietary LLM performance through the addition of reasoning, additional training data, and new techniques have expanded effectiveness, particularly in low-data environments and interdisciplinary research. As LLMs continue to improve, their integration into scientific workflows presents both new opportunities and new challenges, requiring ongoing exploration, continued refinement, and further research to address reliability, interpretability, and reproducibility.

Computer Science↗