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NASA NTRSDate not supplied

LSKnowledge: Nexus for Transformative Scientific Discoveries and Enhanced Information Retrieval in NASA Life Sciences Portal

We stand at the brink of an extraordinary transformation in the field of AI, driven by the convergence of generative AI and semantic technologies (e.g., knowledge graphs). This fusion holds immense potential and could redefine the future of scientific exploration, particularly in the realm of life sciences research. In this context, we shed light on the pivotal roles that Large Language Models (LLMs) and semantic technologies will play in advancing research, unearthing and comprehending life sciences information through innovative approaches, and empowering researchers to extract insights from NASA's extensive Life Sciences Data Archive. Within the NASA Life Sciences Portal (NLSP), the integration of LLMs and semantic technologies unlocks several advanced capabilities. First and foremost, it equips scientists with sophisticated tools to manage the ever-expanding wealth of scientific literature and data. Furthermore, it facilitates the creation of knowledge graphs that visually represent intricate relationships among biological entities, enabling comprehensive systems-level analysis. Additionally, the fusion of generative AI (including LLMs) and semantic technology can significantly benefit NASA's life sciences research by enhancing information retrieval and hypothesis generation. These tools enhance natural language understanding, facilitating knowledge discovery within NLSP. The overarching vision is to establish a cohesive knowledge ecosystem within NLSP, harnessing the power of LLMs and semantic technologies to synthesize and cross-reference data from diverse missions, disciplines, and research domains. This holistic approach ultimately deepens our understanding of how space environments impact life sciences data. To advance this initiative, we have launched LSKnowledge, aimed at enhancing the information retrieval capabilities of NLSP. In the short term, our primary goal is to develop a robust semantic search system. This system will empower HRP (Human Research Program) researchers to navigate NLSP data repositories more efficiently and precisely, catalyzing the process of hypothesis formation and scientific breakthroughs. To achieve this, we have employed pre-trained LLMs as part of a semantic search tool that can rank and highlight the most relevant records for user queries. To assess the tool's performance, we have curated a set of approximately 200 queries from subject matter experts (SMEs) and manually ranked the top records retrieved by both the current search system and the new semantic search, using SME judgments as the gold standard for relevancy. Herein, we present the results of our comparative analysis and illustrate how these findings have informed the fine-tuning of the system for enhanced performance. In the long term, our objectives include 1) retrieving publicly available information and integrating it with NLSP data to provide more precise answers to user queries, and 2) incorporating non-textual information from the NLSP database into our approach. In conclusion, the fusion of LLMs and semantic technologies within NLSP represents a pioneering stride towards reshaping the landscape of scientific discovery. This synergy not only equips researchers with powerful tools to navigate the burgeoning sea of information but also facilitates a deeper understanding of complex biological relationships, all while accelerating hypothesis generation and knowledge discovery. Through our initiative, LSKnowledge, we are committed to continually refining and expanding these capabilities, with the aim of not only enhancing information retrieval but also integrating diverse data sources to provide more precise insights. In the grand vision, NLSP strives to become the cornerstone of a comprehensive knowledge ecosystem, unraveling the enigmatic intricacies of life sciences phenomena in the context of space environments.

Life Sciences↗

DOE OSTI2019

ASCR Workshop on In Situ Data Management: Enabling Scientific Discovery from Diverse Data Sources [Brochure]

In January 2019, the U.S. Department of Energy (DOE), Office of Science (SC) program in Advanced Scientific Computing Research (ASCR) convened a workshop to identify priority research directions (PRDs) for In Situ Data Management (ISDM). The workshop defined ISDM as the practices, capabilities, and procedures to control the organization of data and enable the coordination and communication among heterogeneous tasks, executing simultaneously in a high-performance computing (HPC) system, cooperating toward a common objective.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗

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↗

NASA NTRS2013

International Space Station Accomplishments Update: Scientific Discovery, Advancing Future Exploration, and Benefits Brought Home to Earth

Throughout the history of the International Space Station (ISS), crews on board have conducted a variety of scientific research and educational activities. Well into the second year of full utilization of the ISS laboratory, the trend of scientific accomplishments and educational opportunities continues to grow. More than 1500 investigations have been conducted on the ISS since the first module launched in 1998, with over 700 scientific publications. The ISS provides a unique environment for research, international collaboration and educational activities that benefit humankind. This paper will provide an up to date summary of key investigations, facilities, publications, and benefits from ISS research that have developed over the past year. Discoveries in human physiology and nutrition have enabled astronauts to return from ISS with little bone loss, even as scientists seek to better understand the new puzzle of "ocular syndrome" affecting the vision of up to half of astronauts. The geneLAB campaign will unify life sciences investigations to seek genomic, proteomic, and metabolomics of the effect of microgravity on life as a whole. Combustion scientists identified a new "cold flame" phenomenon that has the potential to improve models of efficient combustion back on Earth. A significant number of instruments in Earth remote sensing and astrophysics are providing new access to data or nearing completion for launch, making ISS a significant platform for understanding of the Earth system and the universe. In addition to multidisciplinary research, the ISS partnership conducts a myriad of student led research investigations and educational activities aimed at increasing student interest in science, technology, engineering and mathematics (STEM). Over the past year, the ISS partnership compiled new statistics of the educational impact of the ISS on students around the world. More than 43 million students, from kindergarten to graduate school, with more than 28 million teachers located in 49 countries have participated in some aspect of ISS educational activities. These activities include student-developed investigations, education competitions, and classroom versions of ISS investigations, participating in ISS investigator experiments, ISS hardware development, educational demonstrations, and cultural activities. Through the many inquiry-based educational activities, students and teachers are encouraged to participate in the ISS program thus motivating the next generation of students to pursue careers in STEM.

Thumm, Tracy↗

DOE OSTI2019

Storage Systems and Input/Output: Organizing, Storing, and Accessing Data for Scientific Discovery. Report for the DOE ASCR Workshop on Storage Systems and I/O. [Summary Brief]

In September, 2018, the Department of Energy, Office of Science, Advanced Scientific Computing Research Program convened a workshop to identify key challenges and define research directions that will advance the field of storage systems and I/O over the next 5-7 years.The workshop concluded that addressing these combined challenges and opportunities requires tools and techniques that greatly extend traditional approaches and require new research directions. Key research opportunities were identified. This summary brief highlights three areas: The Impact of Storage and I/O, Research Opportunities, and, A new and productive Storage and I/O Environment. The full report from the workshop can be viewed at DOI: 10.2172/1491994.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2019

Isotope harvesting at FRIB: additional opportunities for scientific discovery

The upcoming Facility for Rare Isotope Beams (FRIB) at Michigan State University provides a new opportunity to access some of the world's most specialized scientific resources: radioisotopes. An excess of useful radioisotopes will be formed as FRIB fulfills its basic science mission of providing rare isotope beams. In order for the FRIB beams to reach high-purity, many of the isotopes are discarded and go unused. If harvested, the unused isotopes could enable new research for diverse applications ranging from medical therapy and diagnosis to nuclear security. Given that FRIB will have the capability to create about 80% of all possible atomic nuclei, harvesting at FRIB will provide a fast path for access to a vast array of isotopes of interest in basic and applied science investigations. To fully realize this opportunity, infrastructure investment is required to enable harvesting and purification of otherwise unused isotopes. An investment in isotope harvesting at FRIB will provide a powerful resource for development of crucial isotope applications. In 2010, the United States Department of Energy Office of Science, Nuclear Physics, sponsored the first 'Workshop on Isotope Harvesting at FRIB', convening researchers from diverse fields to discuss the scientific impact and technical feasibility of isotope harvesting. Following the initial meeting, a series of biennial workshops was organized. At the fourth workshop, at Michigan State University in 2016, the community elected to prepare a formal document to present their findings. As a result, this report is the output of the working group, drawing on contributions and discussions with a broad range of scientific experts.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2018

Storage Systems and Input/Output: Organizing, Storing, and Accessing Data for Scientific Discovery. Report for the DOE ASCR Workshop on Storage Systems and I/O. [Full Workshop Report]

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

Novel color center platforms enabling fundamental scientific discovery

Abstract Color centers are versatile systems that generate quantum light, sense magnetic fields and produce spin‐photon entanglement. We review how these properties have pushed the limits of fundamental knowledge in a variety of scientific disciplines, from rejecting local‐realistic theories to sensing superconducting phase transitions. In the light of recent progress in material processing and device fabrication, we identify new opportunities for interdisciplinary fundamental discoveries in physics and geochemistry. image

24 POWER TRANSMISSION AND DISTRIBUTION↗

NASA NTRS2006

Laboratory Astrophysics: Enabling Scientific Discovery and Understanding

NASA's Science Strategic Roadmap for Universe Exploration lays out a series of science objectives on a grand scale and discusses the various missions, over a wide range of wavelengths, which will enable discovery. Astronomical spectroscopy is arguably the most powerful tool we have for exploring the Universe. Experimental and theoretical studies in Laboratory Astrophysics convert "hard-won data into scientific understanding". However, the development of instruments with increasingly high spectroscopic resolution demands atomic and molecular data of unprecedented accuracy and completeness. How to meet these needs, in a time of severe budgetary constraints, poses a significant challenge both to NASA, the astronomical observers and model-builders, and the laboratory astrophysics community. I will discuss these issues, together with some recent examples of productive astronomy/lab astro collaborations.

Kirby, K.↗

DOE OSTI2023

Empowering Scientific Discovery Through Computing at the Advanced Photon Source

This paper explores the challenges and solutions for managing and processing the vast amount of data generated by the Advanced Photon Source (APS), a synchrotron light source facility producing ultra-bright x-rays for diverse scientific domains. With 68 experimental beamlines covering materials research, biology, and more, the APS serves a wide user base across academia, government, and industry. The ongoing upgrade of the APS storage ring and installation of new instruments will amplify data generation and processing demands. This paper discusses the approach to address these demands through automated data processing using standardized workflows that produce faster scientific insights. The APS Data Management System coordinates various data related tasks to manage storage, data transfer, metadata cataloging, data processing, and interfaces with tools provided by Globus. Through integration with the Argonne Leadership Computing Facility (ALCF), APS users can efficiently access high-performance computing resources. Standardized workflows have led to reduced computational burdens on scientists and greater accessibility of high performance computing resources. We demonstrate how standardization and collaboration enable scientists to rapidly convert raw data into meaningful scientific results, establishing a streamlined path from data collection to analysis and ultimately to publication.

Parraga, Hannah↗

DOE OSTI2022

Towards Adaptive Decision Support: A Perspective from Intelligent and Annotated Visual Analytics for Exploring Big Urban Mobility Data

Explore the source record for details and available documents.

Xu, Haowen↗

DOE OSTI2024

Data-Driven Supervised Dimension Reduction for Scientific Discovery (LDRD QTI Report)

This report summarizes the findings of a four months FY24 Advanced Science & Technology (AS&T) LDRD Quick Targeted Investigation (QTI) project focused on the exploration of supervised dimension reduction approaches based on autoencoders. Autoencoders have been extensively employed in literature for unsupervised learning tasks, however, their use for supervised regression tasks, which are common within scientific applications, has been limited. Motivated by linear dimension reduction strategies like Active Subspaces and Adaptive Basis, we explored the possibility of employing autoencoders to discover a non-linear manifold able to represent the original function in fewer dimensions. In this report, we discuss a neural network architecture and we perform a numerical campaign on several problems ranging from simple two-dimensional functions to a model problem for magnetohydrodynamics in five dimensions. In our preliminary results, we show that the proposed approach is found to be superior to linear dimension reduction strategies in representing the target function even with a single latent variable.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Plasma: at the frontier of scientific discovery

In February 2015, the Department of Energy, Office of Science, Fusion Energy Sciences (FES)” announced the formation of four technical workshops “to seek community engagement and input for future program planning activities.” Three of these workshops focused on the science and technical challenges we must address to meet the emerging needs of ITER and to allow exploration of fusion burning plasma physics in the laboratory. This report represents the work of the fourth workshop, which focused on the broader scientific opportunities at the frontiers of plasma science. The primary charge to the Panel authoring this report was “to ‘identify’ compelling scientific challenges at the frontiers of plasma physics” and “to ‘identify’ research tools and capabilities to address these challenges in the next decade.” This report aims to inform the newly constituted FES Program in Frontiers of Plasma Science on science opportunities and strategy moving forward. This program was created in FY2015 by combining topics from general plasma science, high energy density laboratory plasmas, and exploratory magnetized plasma into a single program area. The Executive Summary, chapter introductions and sidebars to the chapter are intended to provide more general overviews of the findings of the panel. The chapters provide more details following these themes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2015

Applications of Fusion Energy Sciences Research - Scientific Discoveries and New Technologies Beyond Fusion

Since the 1950s, scientists and engineers in the U.S. and around the world have worked hard to make an elusive goal to be achieved on Earth: harnessing the reaction that fuels the stars, namely fusion. Practical fusion would be a source of energy that is unlimited, safe, environmentally benign, available to all nations and not dependent on climate or the whims of the weather. Significant resources, most notably from the U.S. Department of Energy (DOE) Office of Fusion Energy Sciences (FES), have been devoted to pursuing that dream, and significant progress is being made in turning it into a reality. However, that is only part of the story. The process of creating a fusion-based energy supply on Earth has led to technological and scientific achievements of far-reaching impact that touch every aspect of our lives. Those largely unanticipated advances, spanning a wide variety of fields in science and technology, are the focus of this report. There are many synergies between research in plasma physics (the study of charged particles and fluids interacting with self-consistent electric and magnetic fields), high-energy physics, and condensed matter physics dating back many decades. For instance, the formulation of a mathematical theory of solitons, solitary waves which are seen in everything from plasmas to water waves to Bose-Einstein Condensates, has led to an equal span of applications, including the fields of optics, fluid mechanics and biophysics. Another example, the development of a precise criterion for transition to chaos in Hamiltonian systems, has offered insights into a range of phenomena including planetary orbits, two-person games and changes in the weather. Seven distinct areas of fusion energy sciences were identified and reviewed which have had a recent impact on fields of science, technology and engineering not directly associated with fusion energy: Basic plasma science; Low temperature plasmas; Space and astrophysical plasmas; High energy density laboratory plasmas and inertial fusion energy; Particle accelerator technology; Fusion nuclear science; and Magnetically confined plasmas. Individual sections within the report summarize applications associated with each of these areas. These sections were also informed by a survey that went out to the community, and the subcommittee wishes to thank those who responded, as well as to the national labs and universities that contributed photographs.

43 PARTICLE ACCELERATORS↗

DOE OSTI2019

A Global Hydrologic Framework to Accelerate Scientific Discovery

With the ability to simulate historical and future global water availability on a monthly time step at a spatial resolution of 0.5 geographic degree, the Python package Xanthos version 1 provided a solid foundation for continuing advancements in global water dynamics science. The goal of Xanthos version 2 was to build upon previous investments by creating a Python framework where core components of the model (potential evapotranspiration (PET), runoff generation, and river routing) could be interchanged or extended without having to start from scratch. Xanthos 2 utilizes a component-style architecture which enables researchers to quickly incorporate and test cutting-edge research in a stable modeling environment prebuilt with diagnostics. Major advancements for Xanthos 2 were also achieved by the creation of a robust default configuration with a calibration module, hydropower modules, and new PET modules, which are now available to the scientific community.

GCAM, Xanthos, global hydrology, modeling↗

NASA NTRS2005

Learning from the Mars Rover Mission: Scientific Discovery, Learning and Memory

Purpose: Knowledge management for space exploration is part of a multi-generational effort. Each mission builds on knowledge from prior missions, and learning is the first step in knowledge production. This paper uses the Mars Exploration Rover mission as a site to explore this process. Approach: Observational study and analysis of the work of the MER science and engineering team during rover operations, to investigate how learning occurs, how it is recorded, and how these representations might be made available for subsequent missions. Findings: Learning occurred in many areas: planning science strategy, using instrumen?s within the constraints of the martian environment, the Deep Space Network, and the mission requirements; using software tools effectively; and running two teams on Mars time for three months. This learning is preserved in many ways. Primarily it resides in individual s memories. It is also encoded in stories, procedures, programming sequences, published reports, and lessons learned databases. Research implications: Shows the earliest stages of knowledge creation in a scientific mission, and demonstrates that knowledge management must begin with an understanding of knowledge creation. Practical implications: Shows that studying learning and knowledge creation suggests proactive ways to capture and use knowledge across multiple missions and generations. Value: This paper provides a unique analysis of the learning process of a scientific space mission, relevant for knowledge management researchers and designers, as well as demonstrating in detail how new learning occurs in a learning organization.

Linde, Charlotte↗

NASA NTRSDate not supplied

Using XR for Improving Scientific Discovery With Numerical Weather Models

Earth science (ES) digital twins will help us understand the complex interactions and interrelationships that make up our Earth system and the impacts of earth science phenomena on it. Our work addresses two underdeveloped areas in current ES digital twin work: improving the understanding and interaction with ES model outputs by using Virtual and Mixed Reality (XR) tools and improving the non-intuitive mapping of continuous ES natural phenomena to gridded reference frames in current numerical models. Traditionally, scientists working on ES view and analyze the results of calculated or measured observables with static 1-dimensional (1D), 2D or 3D plots displayed on flat computer screens or paper. Using such limited mediums, it can be very difficult to identify, track and understand the evolution of key features due to poor viewing angles and the nature of flat computer screens. In addition, numerical models, such as the NASA Goddard Earth Observing System (GEOS) ES model, are almost exclusively formulated, visualized and analyzed in an Eulerian reference frame with fixed grid points in space and time. However, ES phenomena such as convective clouds, hurricanes and wildfire smoke plumes are visualized and analyzed in a Lagrangian reference frame: therefore it is often difficult and unnatural to understand these phenomena in relation to each other, visualized either in an Eulerian or Lagrangian context. In 3D visualizations, data generally takes one of three forms: gridded (e.g., voxelized) data, where space is divided into regions; point clouds, where data is represented as a set of points; and meshes, where objects are rendered as surfaces composed of small polygons (usually triangles). A gridded, Eulerian reference frame has been the default representation for the 2D visual analysis of atmospheric data in part because the numerical methods used to generate atmospheric model data in the first place use a gridded approach, with equations defining the relationships between the physical variables in each of a grid's cells across successive timesteps. In our work, we are particularly interested in data from GEOS. Another reason why gridded representations tend to be used for visualizing data from such models is because trajectories are difficult to interpret from representations on 2D surfaces, due to line-of-sight ambiguity. Instead of a fixed grid from GEOS, we embed a trajectory model to simulate particles' movement throughout a GEOS run. We then ingest these particle trajectories as animated point clouds with a NASA open source XR toolkit, the Mixed Reality Exploration Toolkit (MRET), and merge GEOS data with ES phenomena data onto one combined visualization that the user can intuitively interact with. Efficient rendering of arbitrarily large point clouds is an ongoing challenge being addressed by the computer science community, with the GPU-based optimizations and efficient GPU memory utilization a common theme of recent advances, especially for XR, where sustained high frame rate is mandatory to save the user from suffering due to simulation sickness. In this work, we describe and evaluate our progress in choosing and implementing appropriate methods for rendering arbitrarily large point clouds within MRET for XR. While tracking the XR headset enables the immersion of a user within a 3D scene of a data visualization, tracking of XR handheld controllers or user’s hands enables us to implement intuitive user interactions with the visualized datasets. Conventional tools require a user working with an ES visualization to conduct many interactions to commit their intended selections or manipulations with a visualized dataset; for example to specify a set of points in 3D space. Doing so in a 2D flat screen interface has traditionally required specifying a set of points in three distinct 2D coordinate systems (XY, XZ, and YZ), which is cumbersome. In other scientific domains, it has been shown that specifying or selecting a location or volume in XR using handheld controllers or tracked hands allows for greater speed and accuracy. We anticipate the same will hold true for atmospheric data, and we will share initial results of measuring the utility of such an interface. Notably, as the data being visualized is generated by GEOS as a prediction based on initial conditions, an intended application of our tool is to serve as part of an iterative feedback loop. Through XR, a scientist will review and manipulate a GEOS model run, modifying the conditions as needed to do subsequent runs of GEOS. Thereby, XR-based improvements to speed and accuracy of 3D tagging of points minimizes the effort required by both the scientist and the computer cluster conducting the necessary calculations.

Thomas Grubb↗

DOE OSTI2023

Combining data and theory for derivable scientific discovery with AI-Descartes

Abstract Scientists aim to discover meaningful formulae that accurately describe experimental data. Mathematical models of natural phenomena can be manually created from domain knowledge and fitted to data, or, in contrast, created automatically from large datasets with machine-learning algorithms. The problem of incorporating prior knowledge expressed as constraints on the functional form of a learned model has been studied before, while finding models that are consistent with prior knowledge expressed via general logical axioms is an open problem. We develop a method to enable principled derivations of models of natural phenomena from axiomatic knowledge and experimental data by combining logical reasoning with symbolic regression. We demonstrate these concepts for Kepler’s third law of planetary motion, Einstein’s relativistic time-dilation law, and Langmuir’s theory of adsorption. We show we can discover governing laws from few data points when logical reasoning is used to distinguish between candidate formulae having similar error on the data.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗