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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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At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Enabling Space Biological Knowledge Discovery Through Image and Video Data Sharing

Increased biomedical risks associated with deep space crewed missions (cis-Lunar, Mars transit/surface) require development of health countermeasures, novel ecosystem support, risk modeling, and fundamental space biological knowledge discovery. Molecular-omics, physiological-phenotypic-behavioral, and environmental-radiation telemetry data from space biological and health studies are needed for reuse by scientists to address these tasks. The data as well as space-relevant biospecimens are being made more findable, accessible, interoperable, and reusable through NASA’s Open Science Data Repository (OSDR). This new OSDR umbrella grouping includes NASA GeneLab, the NASA Ames Life Sciences Data Archive (ALSDA), and the NASA Biological Institutional Scientific Collection. The OSDR system design appropriately handles metadata and processed-tabular results from ALSDA studies collected from space experiments. But raw and processed ALSDA bioimage and video datasets require an expansion of OSDR’s data architecture to handle ingestion, curation, and egress. The academic-industry bioimaging field saw a scientific renaissance in the past several years through leveraging open-source software, international collaborations, machine learning, and other open science/programming approaches. As crewed missions and more biological experiments are on the deep space horizon, OSDR is embracing data stewardship through listening to feedback from subject matter experts and designing an expanded architecture which is appropriate for NASA’s goals to enable analysis and reuse of bioimaging and video data for the public science community.Discovery Through Image and Video Data Sharing

space biology↗
DOE OSTI2022

Defect identification in simulated Bragg coherent diffraction imaging by automated AI

X-ray Bragg coherent diffraction imaging is a powerful technique for operando and in situ materials characterization and provides a unique means of quantifying the influence of one-dimensional (1D) and two-dimensional (2D) material defects on material response. However, obtaining full images from raw x-ray diffraction data is nontrivial and computationally intensive, precluding real-time experimental feedback. Here, we present a machine learning approach to identify the presence of crystalline line defects (edge and screw) in samples from the raw, 2D, coherent diffraction data without the need for image reconstruction through iterative phase retrieval. Further, we compare different approaches to designing neural networks for this application and demonstrate the potential of automated ML (autoML) approaches.

36 MATERIALS SCIENCE↗
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↗
NASA NTRSDate not supplied

An Open-Science Approach to Address Individual Response to Simulated GCR In Genetically Diverse Populations of Mice and Humans

This project addresses the challenge of understanding and predicting individual radiation sensitivity by integrating genetics, demographics and biomarker characteristics across species (mice and humans). We hypothesize that ex vivo DNA repair response to GCR components is a central determinant of cancer risk from space radiation and can serve as a biomarker of radiation risk in combination with genetics. Automated image quantification of 53BP1+ radiation-induced foci (RIF) during the first 4-48 h post-irradiation was performed as a function of dose and LET in non-immortalized primary skin fibroblasts derived from 76 mice across 15 strains (5 inbred reference strains and 10 collaborative-cross strains) exposed to X rays (0.1, 1 and 4 Gy), 350 MeV/n 40Ar and 600 MeV/n 56Fe (1.1 and 3 particles/100sq. μm), as well as in peripheral blood mononuclear cells (PBMCs) from 768 healthy donors (matched ethnicity, 50/50 male/female, 18-70 years old) exposed to gamma rays (0.1 and 1 Gy), 350 MeV/n 28Si, 350 MeV/n 40Ar and 600 MeV/n 56Fe (1.1 and 3 particles/100sq. μm). A genome-wide association study (GWAS) was performed on the mouse strains between DNA damage responses to space radiation and single nucleotide polymorphisms (SNPs). We found SNPs, which were significantly associated to the RIF phenotype, mapped to genes and pathways that are functionally linked to health hazards for deep space exploration (e.g. carcinogenesis, nervous system damage and immune dysfunction). Some of these SNPs were located within protein coding regions, potentially interfering with protein functions and providing promising genetic targets for countermeasures. We also found correlations between both spontaneous and radiation-induced DNA damage and SNPs mapped to pathways associated with cellular metabolism. GWAS is undergoing for the human data. All data have been made available via the NASA Space Biology Open-Science database (genelab.nasa.gov) and we will discuss how various genomic and transcriptomic datasets can be accessed for modeling and integrated using machine learning methods for discovering new radiation biology.

Sylvain V Costes↗
NASA NTRSDate not supplied

Exploring Anomalous PM 2.5 from Wildfires and Dust Storms using Data and Services at NASA GES DISC

The presence of fine particles in the atmosphere with a diameter of less than 2.5 µm, called particulate matter 2.5 (PM 2.5 ), poses a significant threat to human health as a criteria air pollutant. Fortunately, NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC) provides easy access to several PM 2.5 concentration products. These datasets include the reanalysis of global hourly and monthly aerosol components including PM 2.5 data from the Modern-Era Retrospective analysis for Research and Applications, version 2 (MERRA-2), as well as 3-hourly real-time ensemble forecasts of PM 2.5 from the Hazardous Air Quality Ensemble System (HAQES). The HAQES products are developed by the George Mason University Air Quality Laboratory as part of NASA's Health Air Quality Applied Science Team (HAQAST). The GES DISC is actively collaborating with scientists in the HAQAST program to further expand air quality data collections. Two new datasets are currently being archived: one is the machine learning-based global hourly PM 2.5 derived from MERRA-2; the other is the localized data (NO 2 , O 3 , and PM 2.5 ) time series derived from NASA's GEOS Composition Forecasting (GEOS-CF) system. In this presentation, we will explore the spatial patterns and long-distance transport characteristics of elevated PM 2.5 during extreme pollution events, such as the June 2023 Canadian wildfires, which are still active at the time of writing; and severe spring dust storms in 2023 over Asia. To gain comprehensive insights, we will utilize various PM 2.5 data in conjunction with satellite-observed aerosol data from TROPOspheric Monitoring Instrument (TROPOMI) on Sentinel-5P. The primary focus of this presentation will be to demonstrate effective use of data tools and services to visualize and explore extreme air pollution phenomena. Additionally, we will provide guidance on how users can download specific data of interest, facilitating further analysis and research in this critical area.

air quality↗
DOE OSTI2020

Replicating Machine Learning Experiments in Materials Science

Transparency and reproducibility are important aspects of validation for Machine Learning (ML) models that are not fully understood and applies independently of the application domain.We offer a case study of reproducibility that highlights the challenges encountered when attempting to reproduce analyzes obtained with Machine Learning methods in materials informatics. Our study explores prediction results obtained with ML models and issues in training data serving as input. We discuss challenges related to theory-driven and numerical errors in training data, lack of reproducibility across platforms and versions, and effects of randomness when varying hyperparameters. In addition to model accuracy, a main metric of interest in the ML community, our results show that model sensitivity may be equally important for applying ML in domain applications such a materials science.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Machine-Learning for Excited-State Dynamics

The primary objective of this computational chemistry sciences team is to design a machine learning NAMD environment that will utilize current petascale and future exascale computational capabilities to advance understanding of charge and energy flow in materials. Our machine-learning NAMD environment will 1) integrate advanced NAMD capabilities directly into electronic structure software (e.g., ABINIT, Quantum Espresso, VASP, etc.); 2) merge the preparatory tools of Pychemia into PYXAID and Avogadro environments so that massive data collection from NAMD simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2025

FlowDash Geothermal Energy Enhancer: Where is Next Geothermal Resource? Machine Learning + Multiple Datasets => Geothermal Exploration Indication?

This is the presentation delivered at the 2025 GEODE Datathon competition. GEODE is a consortium of experts that addresses technology and knowledge gaps in geothermal energy, leveraging technology and best practices from the oil and gas industry. NETL team was awarded the 1st place in the engineering track. 2025 GEODE Datathon had a total of 42 teams from top universities and several major industrial companies. This awarded work is founded on a robust idea and innovative approach that uses machine learning coupled to multiple datasets to visualize geothermal “sweet” spots/indications in Great Basin based on the data provided from the GEODE Datathon. The use case also leveraged other datasets and demonstrated insightful and valuable indications for geothermal exploration.

Geothermal energy, Machine learning, Multiple Data↗
NASA NTRS2003

Intelligent machines in the twenty-first century: foundations of inference and inquiry

The last century saw the application of Boolean algebra to the construction of computing machines, which work by applying logical transformations to information contained in their memory. The development of information theory and the generalization of Boolean algebra to Bayesian inference have enabled these computing machines, in the last quarter of the twentieth century, to be endowed with the ability to learn by making inferences from data. This revolution is just beginning as new computational techniques continue to make difficult problems more accessible. Recent advances in our understanding of the foundations of probability theory have revealed implications for areas other than logic. Of relevance to intelligent machines, we recently identified the algebra of questions as the free distributive algebra, which will now allow us to work with questions in a way analogous to that which Boolean algebra enables us to work with logical statements. In this paper, we examine the foundations of inference and inquiry. We begin with a history of inferential reasoning, highlighting key concepts that have led to the automation of inference in modern machine-learning systems. We then discuss the foundations of inference in more detail using a modern viewpoint that relies on the mathematics of partially ordered sets and the scaffolding of lattice theory. This new viewpoint allows us to develop the logic of inquiry and introduce a measure describing the relevance of a proposed question to an unresolved issue. Last, we will demonstrate the automation of inference, and discuss how this new logic of inquiry will enable intelligent machines to ask questions. Automation of both inference and inquiry promises to allow robots to perform science in the far reaches of our solar system and in other star systems by enabling them not only to make inferences from data, but also to decide which question to ask, which experiment to perform, or which measurement to take given what they have learned and what they are designed to understand.

Review↗
NASA NTRS2019

Automatic Anomaly Detection with Machine Learning

This presentation will discuss the following topics: Understanding the benefits of automatic anomaly detection Identifying precursors with algorithms. Overcoming the challenges of data issues in automation.

Data Science↗
DOE OSTI2022

Inferring dark matter substructure with astrometric lensing beyond the power spectrum

Abstract Astrometry—the precise measurement of positions and motions of celestial objects—has emerged as a promising avenue for characterizing the dark matter population in our Galaxy. By leveraging recent advances in simulation-based inference and neural network architectures, we introduce a novel method to search for global dark matter-induced gravitational lensing signatures in astrometric datasets. Our method based on neural likelihood-ratio estimation shows significantly enhanced sensitivity to a cold dark matter population and more favorable scaling with measurement noise compared to existing approaches based on two-point correlation statistics. We demonstrate the real-world viability of our method by showing it to be robust to non-trivial modeled as well as unmodeled noise features expected in astrometric measurements. This establishes machine learning as a powerful tool for characterizing dark matter using astrometric data.

convolutional neural networks (1938)↗
DOE OSTI2025

Keeping LAMMPS cutting edge

Since its inception 30 years ago, LAMMPS has grown to be a world-class molecular dynamics code and a cornerstone of computational materials science research. This project aimed to keep LAMMPS at the forefront of molecular dynamics simulations by adapting LAMMPS to the latest developments in machine learning technology and hardware. Initially, the project set out to provide a unified implementation of active learning for efficient training data generation in LAMMPS, but the research trajectory pivoted to address more immediate and impactful opportunities. On the hardware side, recent record-breaking molecular dynamics simulations were developed on the Cerebras wafer-scale AI chip, and this project has developed an interface between LAMMPS and the hardware-specific molecular dynamics code to accelerate and simplify development and user adoption. On the software side, PyTorch’s Ahead-of-Time (AOT) compilation features promised increased performance for state-of-the-art equivariant neural network potentials, and this project laid the groundwork for their adoption in LAMMPS, resulting in a nearly 20x acceleration in extreme cases. Combined with a comprehensive benchmark study of LAMMPS across all current exascale systems, this project has reinforced LAMMPS’s role as a versatile, high-performance tool for current and future materials science applications.

36 MATERIALS SCIENCE↗
NASA NTRSDate not supplied

Application of Support Vector Regression to Derive Crater Depth/Diameter From Satellite Images

Through the study of impact crater shapes, one can draw important conclusions about the nature and evolution of planetary surfaces [e.g., 1-4].In particular, studying the depth (d) to diameter (D)ratio (d/D) of a population of impact craters, in combination with crater count statistics, can yield valuable insights regarding rates of erosion and burial[5]. Motivated by the great abundance of available planetary surface image data, the goal of this project is to develop an efficient way to estimate d/D from satellite images of impact craters for which stereo information is not available [6]. We set out to develop and train a machine learning algorithm to extract d/D from a dataset of synthetic impact crater images for which model d/D is known. The applications of machine learning to planetary science are numerous and diverse [7], including automatic planetary surface mapping [8] and the detection of impact craters [9]. Our algorithm makes use of Support Vector Regression (SVR), which is a type of Support Vector Machine (SVM) [10, 11].SVMs are a branch of supervised machine learning valued for their straightforward implementation and versatility in solving both classification and regression problems. In regression analysis, an SVR algorithm produces a hyperplane function to fit the training data points, as well as an ε-tube that surrounds the hyperplane. Tunable hyperparameters include the width of the ε-tube (ε) and the amount an algorithm is penalized for points which fall outside the ε-tube.

L R Chin↗
DOE OSTI2023

Attentional Ptycho-Tomography (APT) for three-dimensional nanoscale X-ray imaging with minimal data acquisition and computation time

Abstract Noninvasive X-ray imaging of nanoscale three-dimensional objects, such as integrated circuits (ICs), generally requires two types of scanning: ptychographic, which is translational and returns estimates of the complex electromagnetic field through the IC; combined with a tomographic scan, which collects these complex field projections from multiple angles. Here, we present Attentional Ptycho-Tomography (APT), an approach to drastically reduce the amount of angular scanning, and thus the total acquisition time. APT is machine learning-based, utilizing axial self-Attention for Ptycho-Tomographic reconstruction. APT is trained to obtain accurate reconstructions of the ICs, despite the incompleteness of the measurements. The training process includes regularizing priors in the form of typical patterns found in IC interiors, and the physics of X-ray propagation through the IC. We show that APT with ×12 reduced angles achieves fidelity comparable to the gold standard Simultaneous Algebraic Reconstruction Technique (SART) with the original set of angles. When using the same set of reduced angles, then APT also outperforms Filtered Back Projection (FBP), Simultaneous Iterative Reconstruction Technique (SIRT) and SART. The time needed to compute the reconstruction is also reduced, because the trained neural network is a forward operation, unlike the iterative nature of these alternatives. Our experiments show that, without loss in quality, for a 4.48 × 93.2 × 3.92 µm 3 IC (≃6 × 10 8 voxels), APT reduces the total data acquisition and computation time from 67.96 h to 38 min. We expect our physics-assisted and attention-utilizing machine learning framework to be applicable to other branches of nanoscale imaging, including materials science and biological imaging.

47 OTHER INSTRUMENTATION↗
DOE OSTI2020

Application of Systems Engineering Principles and Techniques in Biological Big Data Analytics: A Review

In the past few decades, we have witnessed tremendous advancements in biology, life sciences and healthcare. These advancements are due in no small part to the big data made available by various high-throughput technologies, the ever-advancing computing power, and the algorithmic advancements in machine learning. Specifically, big data analytics such as statistical and machine learning has become an essential tool in these rapidly developing fields. As a result, the subject has drawn increased attention and many review papers have been published in just the past few years on the subject. Different from all existing reviews, this work focuses on the application of systems, engineering principles and techniques in addressing some of the common challenges in big data analytics for biological, biomedical and healthcare applications. Specifically, this review focuses on the following three key areas in biological big data analytics where systems engineering principles and techniques have been playing important roles: the principle of parsimony in addressing overfitting, the dynamic analysis of biological data, and the role of domain knowledge in biological data analytics.

dynamic analysis↗
DOE OSTI2020

Machine learning and ligand binding predictions: A review of data, methods, and obstacles

We report that computational predictions of ligand binding is a difficult problem, with more accurate methods being extremely computationally expensive. The use of machine learning for drug binding predictions could possibly leverage the use of biomedical big data in exchange for time-intensive simulations. This paper reviews current trends in the use of machine learning for drug binding predictions, data sources to develop machine learning algorithms, and potential problems that may lead to overfitting and ungeneralizable models. A few popular datasets that can be used to develop virtual high-throughput screening models are characterized using spatial statistics to quantify potential biases. We can see from evaluating some common benchmarks that good performance correlates with models with high-predicted bias scores and models with low bias scores do not have much predictive power. A better understanding of the limits of available data sources and how to fix them will lead to more generalizable models that will lead to novel drug discovery.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

AI-Science for Performance Optimization and Diagnosis of Science Instrument Federations

Next generation of science workflows are expected to be executed over complex federations composed of supercomputers, science instruments, storage systems and networks, with new additions of the edge and cloud systems and services. The sheer complexity of these multi-domain federations makes it hard to manage them and optimize their performance, as small impedance mismatches (that can dynamically develop between systems) could drastically degrade the entire federation performance. Recent proliferation of Software Defined Everything (SDX) technologies combined with containerization frameworks provide custom instruments that can monitor and collect critical measurements at various levels to support diagnoses and performance optimization; but their data too enormous for human operators and analysts to process and generate decisions. Machine Learning (ML) methods that extract critical parameters, relationships and trends from the data offer general solutions. Artificial Intelligence (AI) and ML methods must be custom-developed for these problems based on solid, rigorous foundations, since black-box approaches are often ineffective and unsound.We propose to develop comprehensive AI-Science for the performance of science federations to (i) monitor and control storage, networks, experiments, and computing systems across multiple domains via softwarization layers, at speeds and scales orders of magnitude superior to current practice, (ii) optimally realize and orchestrate complex workflows with high performance by using dynamic state and performance estimation methods, and (iii) aggregate measurements across sites and time to develop infrastructure-level profiles, optimizations and diagnoses using AI-Science based on foundational principles from ML, game theory, and information fusion areas.

Rao, Nageswara↗