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NASA NTRSDate not supplied

NASA biological and physical sciences databases: who’s the FAIRest of them all?

Conceptual models are a key part of the foundation of scientific study. Scientific data discovery and retrieval are often inaccurate and incomplete because these models are not sufficiently well-incorporated into data retrieval systems. Systems often don’t provide the necessary tools to those producing scientific data to fully and unambiguously annotate them and the result is consumers of the data cannot find them efficiently. The capability of data archives to provide these tools to link data to underlying conceptual models is one of dimensions of the recently developed “FAIR” principles (https://www.go-fair.org/fair-principles/ ), and is key to many automated processes being able to operate on these data, particularly analytics involving artificial intelligence. We used an open-source web service to measure the FAIR compliance of the three data archives operated by NASA for the biological and physical sciences: the Life Sciences Data Archive, the Physical Sciences Informatics database, and GeneLab. The service ingests references to data sets in these archives, and then executes domain-non-specific examinations of these data and metadata that test compliance to the FAIR principles. Of the 22 metrics tested, GeneLab passed 11 (50%), and PSI and LSDA each passed 7 (32%). These data were gathered using only one representative data set from each archive and we anticipate variability in results as we continue to apply these metrics to other data. A preliminary study of the failure traces for each metric suggests there is a wide range of effort and complexity in the enhancements required for each system to elevate FAIR compliance, and this is the subject of continued investigation. This information has been and will likely continue to be important information in planning these enhancements, with the goal of increased readiness of the data for automated processes.

database↗

DOE OSTI2020

Physically Interpretable Neural Networks for the Geosciences: Applications to Earth System Variability

Abstract Neural networks have become increasingly prevalent within the geosciences, although a common limitation of their usage has been a lack of methods to interpret what the networks learn and how they make decisions. As such, neural networks have often been used within the geosciences to most accurately identify a desired output given a set of inputs, with the interpretation of what the network learns used as a secondary metric to ensure the network is making the right decision for the right reason. Neural network interpretation techniques have become more advanced in recent years, however, and we therefore propose that the ultimate objective of using a neural network can also be the interpretation of what the network has learned rather than the output itself. We show that the interpretation of neural networks can enable the discovery of scientifically meaningful connections within geoscientific data. In particular, we use two methods for neural network interpretation called backward optimization and layerwise relevance propagation, both of which project the decision pathways of a network back onto the original input dimensions. To the best of our knowledge, LRP has not yet been applied to geoscientific research, and we believe it has great potential in this area. We show how these interpretation techniques can be used to reliably infer scientifically meaningful information from neural networks by applying them to common climate patterns. These results suggest that combining interpretable neural networks with novel scientific hypotheses will open the door to many new avenues in neural network‐related geoscience research.

58 GEOSCIENCES↗

DOE OSTI2024

40th Eastern Regional Photosynthesis Conference

Photosynthesis has shaped global biochemistry and geochemistry in its production of almost all atmospheric oxygen, and almost every electron that supports the processes of life on Earth was extracted from water by the light-dependent reactions, many of which were then used to fix carbon in the dark reactions. To humans, that fixed carbon is the originator of all fossil fuels and a critical step in conversion of solar photons to human-usable energy. The Eastern Regional Photosynthesis Conference (ERPC) serves as a forum for advancement of science in this field. As a regional-level conference, it specializes in providing developmental opportunities for early-career researchers from undergraduates to pre-tenure professors, as well as broadening the field in terms of participating scholars and research approaches to produce new interdisciplinary collaborations. Funding from the Department of Energy is used to mitigate registration and attendance costs for junior (non-PI) researchers. The ERPC is one of the three regional conferences in the field of photosynthesis research in the United States, alongside the Western Photosynthesis Conference (currently in California) and the Midwestern Photosynthesis Conference (Turkey Run, Indiana). A longstanding incubator for scientific discourse, discovery, and collaborations, this conference has spawned many collaborations which led to Department of Energy-funded projects and brought two full generations of scientists into the field. In 2023 the landmark 40th such conference was held at the Woods Hole Marine Biological Laboratory in Massachusetts. The conference was held April 14th-16th, 2023. The majority of the total attendees are traditionally undergraduates, graduate students, and postdoctoral researchers, whose scientific development greatly benefits from conferences like these. Almost all talks besides the ones given by invited speakers were given by young scientists who benefited from this award. Many posters presented at this conference were also the work of junior scientists. This conference is many young scientists' first exposure to the photosynthesis research community and its primary goal is to provide a positive atmosphere for those attendees, as exemplified by the three poster sessions and dedicated interactions between senior and junior scientists. Additionally, this was the first year of a successful equitable outreach and speaker recruitment plan to provide fair representation at the conference in line with Department of Energy policies and mission. The central focus of the ERPC is the understanding of energy flow from photons to usable chemical products in both natural and artificial photosynthesis. We support the DOE-BES aim of understanding of the biochemical/biophysical processes of photosynthesis to inform new technology development and field applications. This year’s theme, “Photosynthesis Across Scales,” reflected the scope of work being done from the individual exciton to the crop scale. This research encompasses applied biology, chemistry, physics, and materials engineering. The aim of this conference is to present new discoveries, techniques, and questions which can be sourced from any of these disciplines and provide an audience which is broadly qualified to use or respond to these developments.

08 HYDROGEN↗

DOE OSTI2026

Hierarchical Multi-agent Large Language Model Reasoning for Autonomous Heterogeneous Catalyst Discovery

Artificial intelligence is reshaping scientific exploration, but most methods automate procedural tasks without engaging in scientific reasoning, limiting autonomy in discovery. We demonstrate that hierarchical agentic large language model reasoning can efficiently drive simulation and scientific exploration. Across two chemical applications, CO adsorption on Cu surface transition metal adatoms and on M–N–C catalysts, reasoning-guided exploration reduces required atomistic simulations by up to 90% relative to heuristic or random selection. Comparisons across single-agent, multi-agent, and stochastic baselines show that hierarchical strategies yield more coherent and information-efficient search trajectories. Reasoning traces reveal chemically grounded decisions that cannot be explained by semantic bias or stochastic sampling. We realize these agentic reasoning strategies in Materials Agents for Simulation and Theory in Electronic-structure Reasoning (MASTER), a multimodal system that translates natural language into density functional theory workflows. Altogether, multi-agent collaboration accelerates heterogeneous catalyst discovery and marks a step toward more autonomous, reasoning-guided scientific exploration.

30 DIRECT ENERGY CONVERSION↗

DOE OSTI2024

Predictive Phenomics Initiative Project Dataset Catalog Collection

The Predictive Phenomics Science & Technology Initiative (PPI) at Pacific Northwest National Laboratory are tackling the grand challenge of understanding and predicting phenotype by identifying the molecular basis of function and enable function-driven design and control of biological systems. Research projects within this initiative are divided into three Thrust Areas (TAs): TA1) Enhancing Multi-Scale Phenomics Measurements, TA2) Identifying Molecular Patterns of Biological Function, and TA3) Computational Methods - Phenotypic Signatures. In efforts to enable discovery, reproducibility, and reuse of PPI-funded digital research data generated or used through the course of the proposed research-funded lifecycles, all corresponding digital data assets conducted under the Laboratory Directed Research and Development Program at PNNL are linked to this PPI dataset catalog collection.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

Predictive Phenomics Initiative Project Dataset Catalog Collection

59 BASIC BIOLOGICAL SCIENCES↗

NASA NTRS2004

Solar Airplane Concept Developed for Venus Exploration

An airplane is the ideal vehicle for gathering atmospheric data over a wide range of locations and altitudes, while having the freedom to maneuver to regions of scientific interest. Solar energy is available in abundance on Venus. Venus has an exoatmospheric solar flux of 2600 W/m2, compared with Earth's 1370 W/m2. The solar intensity is 20 to 50 percent of the exoatmospheric intensity at the bottom of the cloud layer, and it increases to nearly 95 percent of the exoatmospheric intensity at 65 km. At these altitudes, the temperature of the atmosphere is moderate, in the range of 0 to 100 degrees Celsius, depending on the altitude. A Venus exploration aircraft, sized to fit in a small aeroshell for a "Discovery" class scientific mission, has been designed and analyzed at the NASA Glenn Research Center. For an exploratory aircraft to remain continually illuminated by sunlight, it would have to be capable of sustained flight at or above the wind speed, about 95 m/sec at the cloud-top level. The analysis concluded that, at typical flight altitudes above the cloud layer (65 to 75 km above the surface), a small aircraft powered by solar energy could fly continuously in the atmosphere of Venus. At this altitude, the atmospheric pressure is similar to pressure at terrestrial flight altitudes.

Landis, Geoffrey A.↗

NASA NTRS2006

Representation of Serendipitous Scientific Data

A computer program defines and implements an innovative kind of data structure than can be used for representing information derived from serendipitous discoveries made via collection of scientific data on long exploratory spacecraft missions. Data structures capable of collecting any kind of data can easily be implemented in advance, but the task of designing a fixed and efficient data structure suitable for processing raw data into useful information and taking advantage of serendipitous scientific discovery is becoming increasingly difficult as missions go deeper into space. The present software eases the task by enabling definition of arbitrarily complex data structures that can adapt at run time as raw data are transformed into other types of information. This software runs on a variety of computers, and can be distributed in either source code or binary code form. It must be run in conjunction with any one of a number of Lisp compilers that are available commercially or as shareware. It has no specific memory requirements and depends upon the other software with which it is used. This program is implemented as a library that is called by, and becomes folded into, the other software with which it is used.

James, Mark↗

DOE OSTI2020

Advancing Fusion with Machine Learning Research Needs Workshop Report

Abstract Machine learning and artificial intelligence (ML/AI) methods have been used successfully in recent years to solve problems in many areas, including image recognition, unsupervised and supervised classification, game-playing, system identification and prediction, and autonomous vehicle control. Data-driven machine learning methods have also been applied to fusion energy research for over 2 decades, including significant advances in the areas of disruption prediction, surrogate model generation, and experimental planning. The advent of powerful and dedicated computers specialized for large-scale parallel computation, as well as advances in statistical inference algorithms, have greatly enhanced the capabilities of these computational approaches to extract scientific knowledge and bridge gaps between theoretical models and practical implementations. Large-scale commercial success of various ML/AI applications in recent years, including robotics, industrial processes, online image recognition, financial system prediction, and autonomous vehicles, have further demonstrated the potential for data-driven methods to produce dramatic transformations in many fields. These advances, along with the urgency of need to bridge key gaps in knowledge for design and operation of reactors such as ITER, have driven planned expansion of efforts in ML/AI within the US government and around the world. The Department of Energy (DOE) Office of Science programs in Fusion Energy Sciences (FES) and Advanced Scientific Computing Research (ASCR) have organized several activities to identify best strategies and approaches for applying ML/AI methods to fusion energy research. This paper describes the results of a joint FES/ASCR DOE-sponsored Research Needs Workshop on Advancing Fusion with Machine Learning, held April 30–May 2, 2019, in Gaithersburg, MD (full report available at https://science.osti.gov/-/media/fes/pdf/workshop-reports/FES_ASCR_Machine_Learning_Report.pdf ). The workshop drew on broad representation from both FES and ASCR scientific communities, and identified seven Priority Research Opportunities (PRO’s) with high potential for advancing fusion energy. In addition to the PRO topics themselves, the workshop identified research guidelines to maximize the effectiveness of ML/AI methods in fusion energy science, which include focusing on uncertainty quantification, methods for quantifying regions of validity of models and algorithms, and applying highly integrated teams of ML/AI mathematicians, computer scientists, and fusion energy scientists with domain expertise in the relevant areas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2023

Toward implementing autonomous adaptive data acquisition for scanning hyperspectral imaging of biological systems

Autonomous experimentation is an emerging area of research, primarily related to autonomous vehicles, scientific combinatorial discovery approaches in materials science and drug discovery, and iterative research loops of planning, experimentation, and analysis. However, autonomous approaches developed in these contexts are difficult to apply to high-dimensional mapping technologies, such as scanning hyperspectral imaging of biological systems, due to sample complexity and heterogeneity. We briefly cover the history of adaptive sampling algorithms and surrogate modeling in order to define autonomous adaptive data acquisition as an objective-based, flexible building block for future biological imaging experimentation driven by intelligent infrastructure. We subsequently summarize the recent implementations of autonomous adaptive data acquisition (AADA) for scanning hyperspectral imaging, assess how these address the difficulties of autonomous approaches in hyperspectral imaging, and highlight the AADA design variation from a goal-oriented perspective. Finally, we present a modular AADA architecture that embeds AADA-driven flexible building blocks to address the challenge of time resolution for high-dimensional scanning hyperspectral imaging of nonequilibrium dynamical systems. In our example research-driven experimental design case, we propose an AADA infrastructure for time-resolved, noninvasive, and label-free scanning hyperspectral imaging of living biological systems. This AADA infrastructure can accurately target the correct state of the system for experimental workflows that utilize subsequent expensive, high-information-content analytical techniques.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2026

CVEVOLVE

CVEvolve is an agentic AI system for autonomous algorithm discovery for scientific data processing. It creates workflows where large language model agents freely set up and configure development environments and evaluation harnesses, develop and improve data processing algorithms with designed exploration-exploitation balancing mechanisms, log history and findings in a structured database, and run holdout testing to ensure algorithm generalizability. CVEvolve offers a zero-code interface and does not require users to provide structured data and evaluation scripts.

Cherukara, MatthewJoseph [Argonne National Laborat↗

DOE OSTI2025

Collaborative: in situ visual analytics technologies for extreme scale combustion simulations

This project aims to drastically enhance the usability of in situ analysis and visualization for extreme-scale scientific simulations. Current exascale computing capabilities promise to offer greater predictive ability of simulations and to further push the frontiers of science and technology. However, to validate the simulation output at extreme scale, examine the modeled phenomena, and discover previously unknowns from the output data, the output must be reduced or transformed in situ as it is being generated during the simulation such that the amount of data to examine and store is kept to a minimum. Such in situ approaches allow us to process and analyze the data and any embedded geometry to an extent that would be prohibitively expensive, if not impossible, to perform as a post hoc task. While in situ processing has been demonstrated to be a feasible and promising approach, its full potential has not yet been leveraged. In this project, we have developed comprehensive enhancements to in situ technology based on probability distributions in data. Our research focuses on jointly developing new ways of interacting with massive statistical samples while creatively utilizing new state-of-the-art computational resources to push the boundaries of in situ exploration. Moreover, we have developed new time-dependent techniques to enable previously unattainable capabilities in areas such as intelligent simulation steering and precise feature identification. We have experimentally studied our design and implementation at NERSC and OLCF, and are able to leverage existing in situ infrastructures whenever possible. While the exemplar in this project is combustion, many other fields for which turbulent transport is important, e.g., fusion, climate, astrophysics among others, encounter similar issues as simulations scale up to the exascale. This project shows its potential to generate high impact on DOE missions since the resulting technology promises to improve scientists’ ability to rapidly and correctly interpret and tune extreme-scale simulations, leading to new scientific understanding and advancements.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1985

A New Global Geomorphology?

Geomorphology is entering a new era of discovery and scientific excitement centered on expanding scales of concern in both time and space. The catalysts for this development include technological advances in global remote sensing systems, mathematical modeling, and the dating of geomorphic surfaces and processes. Even more important are new scientific questions centered on comparative planetary geomorphology, the interaction of tectonism with landscapes, the dynamics of late Cenozoic climatic changes, the influence of cataclysmic processes, the recognition of extremely ancient landforms, and the history of the world's hydrologic systems. These questions all involve feedback relationships with allied sciences that have recently yielded profound developments.

Baker, V. R.↗

NASA NTRS1992

Historical Data for Geomagnetic Field Modelling

Described here are the various types of historical data (by which we mean actual measurements of the field taken in the past) which are available for geomagnetic field modelling, concentrating exclusively on observations made prior to the 20th century. These data take quite diverse forms, being derived from voyages of discovery or scientific expeditions, from surveys on land, or from observatories after the formation of the Gottingen Magnetic Union. Toward the latter part of the 19th Century, declination was measured quite regularly by various naval vessels for the purpose of constructing charts. Prior to the invention of a method for measuring field intensities by Gauss in 1832, all measurements were of declination or inclination. During the 19th Century over 40,000 observations are available in one year (the first International Polar Year 1882-83). Prior to 1800 there are only of the order of 12,000 measurements. We discuss measurement methods and the types of instruments used, and focus on specific problems which have been noted, such as those associated with the measurement of intensity on iron ships in the late 19th Century.

Jackson, Andrew↗

NASA NTRSDate not supplied

Scientific Content Curation in an Open Science Era

Today’s open science environment, in combination with the Big Data era, means more scientific data, software, tools, documentation, publications and other resources are available than ever. The promise of the open science era is that scientists will spend less time reinventing the wheel and more time doing actionable research. Yet navigating this vast and complex information landscape can feel overwhelming to scientists trying to get their bearings. In this presentation, we define and discuss the importance of scientific content curation for enhancing discovery and use of scientific data and information. We also share two examples of scientific content curation in action: the Catalog of Archived Suborbital Earth Science Investigations (CASEI) and the Science Discovery Engine (SDE).

Kaylin Bugbee↗

NASA NTRSDate not supplied

Creating a knowledge graph to connect scientific publications and datasets for improving discovery of GES DISC’s data and services

The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) archives and distributes to the public hundreds of Earth Science data collections. These collections are used in research, resulting in thousands of scientific papers published each year. As new users come to GES DISC for the data, it is important for them to understand how these data were used in the prior research. For this we are creating the Knowledge Graph that connects research paper citation and the data collection metadata. The relationships created in the graph have potential for the Web applications that utilize this information to directly connect the paper research to the GES DISC datasets and services. We will demonstrate these relationships using the Web application prototype.

Nathaniel Ross Crosby↗

DOE OSTI2025

Solving sparse finite element problems on neuromorphic hardware

The finite element method (FEM) is one of the most important and ubiquitous numerical methods for solving partial differential equations (PDEs) on computers for scientific and engineering discovery. Applying the FEM to larger and more detailed scientific models has driven advances in high-performance computing for decades. Here we demonstrate that scalable spiking neuromorphic hardware can directly implement the FEM by constructing a spiking neural network that solves the large, sparse, linear systems of equations at the core of the FEM. We show that for the Poisson equation, a fundamental PDE in science and engineering, our neural circuit achieves meaningful levels of numerical accuracy and close to ideal scaling on modern, inherently parallel and energy-efficient neuromorphic hardware, specifically Intel’s Loihi 2 neuromorphic platform. We illustrate extensions to irregular mesh geometries in both two and three dimensions as well as other PDEs such as linear elasticity. Our spiking neural network is constructed from a recurrent network model of the brain’s motor cortex and, in contrast to black-box deep artificial neural network-based methods for PDEs, directly translates the well-understood and trusted mathematics of the FEM to a natively spiking neuromorphic algorithm.

Applied mathematics↗

DOE OSTI2025

AI-Powered Knowledge Graphs for Neuromorphic and Energy-Efficient Computing

The surge in scientific literature obscures breakthroughs and hinders the discovery of new research paths. We propose an artificial intelligence (AI) powered framework using large language models (LLMs) and knowledge graphs (KGs) to automate parts of scientific discovery, focusing on energy-efficient AI circuits. Our hybrid approach combines LLMs, structured data, and ontology-based reasoning to construct a comprehensive knowledge graph that integrates insights across computational neuroscience, spiking neuron models, learning rules, architectural motifs, and neuromorphic device technologies. This multi-domain representation enables the generation of hypotheses that connect biological function with implementable, energy-efficient hardware architectures. Using KG embeddings and graph neural networks, the framework generates hypotheses for novel circuits, validates them through optimization on exascale HPC systems, and with tools like SuperNeuro and Fugu, the most promising designs will be prototyped in hardware. This open-source system aims to accelerate discoveries and bridging neuroscience with hardware innovation, drive collaboration, and unlock new opportunities in low-power AI computing.

Gautam, Ashish [ORNL]↗