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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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A generalized platform for artificial intelligence-powered autonomous enzyme engineering

Proteins are the molecular machines of life with numerous applications in energy, health, and sustainability. However, engineering proteins with desired functions for practical applications remains slow, expensive, and specialist-dependent. Here we report a generally applicable platform for autonomous enzyme engineering that integrates machine learning and large language models with biofoundry automation to eliminate the need for human intervention, judgement, and domain expertise. Requiring only an input protein sequence and a quantifiable way to measure fitness, this automated platform can be applied to engineer a wide array of proteins. As a proof of concept, we engineer Arabidopsis thaliana halide methyltransferase (AtHMT) for a 90-fold improvement in substrate preference and 16-fold improvement in ethyltransferase activity, along with developing a Yersinia mollaretii phytase (YmPhytase) variant with 26-fold improvement in activity at neutral pH. This is accomplished in four rounds over 4 weeks, while requiring construction and characterization of fewer than 500 variants for each enzyme. This platform for autonomous experimentation paves the way for rapid advancements across diverse industries, from medicine and biotechnology to renewable energy and sustainable chemistry.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1987

Space languages

Applications of linguistic principles to potential problems of human and machine communication in space settings are discussed. Variations in language among speakers of different backgrounds and change in language forms resulting from new experiences or reduced contact with other groups need to be considered in the design of intelligent machine systems.

Hays, Dan↗
NASA NTRS2012

Querying Proofs

We motivate and introduce a query language PrQL designed for inspecting machine representations of proofs. PrQL natively supports hiproofs which express proof structure using hierarchical nested labelled trees. The core language presented in this paper is locally structured (first-order), with queries built using recursion and patterns over proof structure and rule names. We define the syntax and semantics of locally structured queries, demonstrate their power, and sketch some implementation experiments.

Aspinall, David↗
DOE OSTI2025

Towards Automated Assessment of Vulnerability Exposures in Security Operations

Current approaches for risk analysis of software vulnerabilities using manual assessment and numeric scoring do not complete fast enough to keep pace with the maintenance work rate to patch and mitigate the vulnerabilities. This paper proposes a new approach to modeling software vulnerability risk in the context of the network environment and firewall configuration. In the approach, vulnerability features are automatically matched up with networking, target asset, and adversary features to determine whether adversaries can exploit a vulnerability. The ability of adversaries to reach a vulnerability is modeled by automatically identifying the network services associated with vulnerabilities through a pipeline of machine learning and natural language processing and automatically analyzing network reachability. Our results show that the pipeline can identify network services accurately. We also find that only a small number of vulnerabilities pose real risks to a system. However, if left unmitigated, adversarial reach to vulnerabilities may extend to nullify the effect of firewall countermeasures.

Huff, Philip↗
DOE OSTI2026

Generalist multimodal AI: A review of architectures, challenges and opportunities

Multimodal models are expected to be a critical component to future advances in artificial intelligence. Here, this field is starting to grow rapidly with a surge of new design elements motivated by the success of foundation models in natural language processing (NLP) and vision. It is widely hoped that further extending the foundation models to multiple modalities (e.g., text, image, video, sensor, time series, graph, etc.) will ultimately lead to generalist multimodal models, i.e. one model across different data modalities and tasks. However, there is little research that systematically analyzes recent multimodal models (particularly the ones that work beyond text and vision) with respect to the underling architecture proposed. Therefore, this work provides a fresh perspective on generalist multimodal models (GMMs) via a novel architecture and training configuration specific taxonomy. This includes factors such as Unifiability, Modularity, and Adaptability that are pertinent and essential to the wide adoption and application of GMMs. The review further highlights key challenges and prospects for the field and guide the researchers into the new advancements.

Artificial intelligence (AI)↗
DOE OSTI2026

Wolf

The Workflow Orchestration Language Framework (WOLF) is an agentic framework grounded in natural language with an architecture inspired by reinforcement learning (RL)—designed to orchestrate, scale, and accelerate complex workflows. The concept of WOLF was born out of the very successful ASC Tri-lab Multi-Agent Design Assistant (MADA) project, but extends beyond its domain-specific design agents to provide a more general and extensible architecture. WOLF capitalizes on the lessons learned from MADA and is fully aligned with Sutton’s The Bitter Lesson—that the most enduring progress in AI comes from general-purpose methods that scale with computation, rather than narrow techniques built on domain-specific human knowledge. In this spirit, WOLF enables agents to autonomously learn workflows, capture strategies as reusable playbooks, and build a growing corpus of interpretable, auditable “wisdom artifacts.” These artifacts, expressed in natural language, bridge human and machine understanding while preserving adaptability and scalability as computational power continues to expand.

Boureima, Ismaeal↗
DOE OSTI2025

Chemical classification program synthesis using generative artificial intelligence

Accurately classifying chemical structures is essential for cheminformatics and bioinformatics, including tasks such as identifying bioactive compounds of interest, screening molecules for toxicity to humans, finding non-organic compounds with desirable material properties, or organizing large chemical libraries for drug discovery or environmental monitoring. However, manual classification is labor-intensive and difficult to scale to large chemical databases. Existing automated approaches either rely on manually constructed classification rules, or are deep learning methods that lack explainability. This work presents an approach that uses generative artificial intelligence to automatically write chemical classifier programs for classes in the Chemical Entities of Biological Interest (ChEBI) database. These programs can be used for efficient deterministic run-time classification of SMILES structures, with natural language explanations. The programs themselves constitute an explainable computable ontological model of chemical class nomenclature, which we call the ChEBI Chemical Class Program Ontology (C3PO). We validated our approach against the ChEBI database, and compared our results against deep learning models and a naive SMARTS pattern based classifier. C3PO outperforms the naive classifier, but does not reach the performance of state of the art deep learning methods. However, C3PO has a number of strengths that complement deep learning methods, including explainability and reduced data dependence. C3PO can be used alongside deep learning classifiers to provide an explanation of the classification, where both methods agree. The programs can be used as part of the ontology development process, and iteratively refined by expert human curators.

Artificial Intelligence↗
DOE OSTI2026

Data for A Generalized Platform for Artificial Intelligence-powered Autonomous Protein Engineering

Proteins are the molecular machines of life with numerous applications in energy, health, and sustainability. However, engineering proteins with desired functions for practical applications remains slow, expensive, and specialist-dependent. Here we report a generally applicable platform for autonomous enzyme engineering that integrates machine learning and large language models with biofoundry automation to eliminate the need for human intervention, judgement, and domain expertise. Requiring only an input protein sequence and a quantifiable way to measure fitness, this automated platform can be applied to engineer a wide array of proteins. As a proof of concept, we engineer Arabidopsis thaliana halide methyltransferase (AtHMT) for a 90-foldimprovement in substrate preference and 16-fold improvement in ethyl-transferase activity, along with developing a Yersinia mollaretii phytase (YmPhytase) variant with 26-fold improvement in activity at neutral pH. This is accomplished in four rounds over 4 weeks, while requiring construction and characterization of fewer than 500 variants for each enzyme. This platform for autonomous experimentation paves the way for rapid advancements across diverse industries, from medicine and biotechnology to renewable energy and sustainable chemistry.

AI/ML↗
DOE OSTI2024

Improving Reliability of Large Language Models for Nuclear Power Plant Diagnostics [Poster]

Large Language Models (LLMs) struggle out of the box when answering factually about detailed questions, especially in domains that are sparsely represented in their training data. This causes hallucinations and reduces reliability making it difficult for them to be used in practice. This work shows that using RAG techniques can improve factual accuracy and reliability, allowing for the application of LLMs in specialized areas, even when those areas that aren’t extensively covered in their initial training.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1966

Linguistics

Studies of Latvian morphophonemics, metrics of Arabic poetry, and intersection of regular languages and languages generated by transformational grammars

LANGUAGE↗
NASA NTRS1984

Speed test results and hardware/software study of computational speed problem, appendix D

The HP9845C is a desktop computer which is tested and evaluated for processing speed. A study was made to determine the availability and approximate cost of computers and/or hardware accessories necessary to meet the 20 ms sample period speed requirements. Additional requirements were that the control algorithm could be programmed in a high language and that the machine have sufficient storage to store the data from a complete experiment.

Source record↗
NASA NTRS1989

NASA-Enhanced Version Of Automatically Programmed Tool Software (APT)

APT code one of most widely used software tools for complex numerically-controlled machining. Both a programming language and software that processes language. Upgrades include super pocket for concave polygon pockets and editor to reprocess cutter location coordinates according to user-supplied commands.

Purves, L. R.↗
NASA NTRS1993

The ParaScope parallel programming environment

The ParaScope parallel programming environment, developed to support scientific programming of shared-memory multiprocessors, includes a collection of tools that use global program analysis to help users develop and debug parallel programs. This paper focuses on ParaScope's compilation system, its parallel program editor, and its parallel debugging system. The compilation system extends the traditional single-procedure compiler by providing a mechanism for managing the compilation of complete programs. Thus, ParaScope can support both traditional single-procedure optimization and optimization across procedure boundaries. The ParaScope editor brings both compiler analysis and user expertise to bear on program parallelization. It assists the knowledgeable user by displaying and managing analysis and by providing a variety of interactive program transformations that are effective in exposing parallelism. The debugging system detects and reports timing-dependent errors, called data races, in execution of parallel programs. The system combines static analysis, program instrumentation, and run-time reporting to provide a mechanical system for isolating errors in parallel program executions. Finally, we describe a new project to extend ParaScope to support programming in FORTRAN D, a machine-independent parallel programming language intended for use with both distributed-memory and shared-memory parallel computers.

Cooper, Keith D.↗
NASA NTRS1990

ART/Ada and CLIPS/Ada

Although they have reached a point of commercial viability, expert systems were originally developed in artificial intelligence (AI) research environments. Many of the available tools still work best in such environments. These environments typically utilize special hardware such as LISP machines and relatively unfamiliar languages such as LISP or Prolog. Space Station applications will require deep integration of expert system technology with applications developed in conventional languages, specifically Ada. The ability to apply automation to Space Station functions could be greatly enhanced by widespread availability of state-of-the-art expert system tools based on Ada. Although there have been some efforts to examine the use of Ada for AI applications, there are few, if any, existing products which provide state-of-the-art AI capabilities in an Ada tool. The goal of the ART/Ada Design Project is to conduct research into the implementation in Ada of state-of-the-art hybrid expert systems building tools (ESBT's). This project takes the following approach: using the existing design of the ART-IM ESBT as a starting point, analyze the impact of the Ada language and Ada development methodologies on that design; redesign the system in Ada; and analyze its performance. The research project will attempt to achieve a comprehensive understanding of the potential for embedding expert systems in Ada systems for eventual application in future Space Station Freedom projects. During Phase 1 of the project, initial requirements analysis, design, and implementation of the kernel subset of ART-IM functionality was completed. During Phase 2, the effort has been focused on the implementation and performance analysis of several versions with increasing functionality. Since production quality ART/Ada tools will not be available for a considerable time, and additional subtask of this project will be the completion of an Ada version of the CLIPS expert system shell developed by NASA. This tool will provide full syntactic compatibility with any eventual products of the ART/Ada design while allowing SSFP developers early access to this technology.

Culbert, Chris↗
NASA NTRS1996

Program Converts VAX Floating-Point Data To UNIX

VAX Floating Point to Host Floating Point Conversion (VAXFC) software converts non-ASCII files to unformatted floating-point representation of UNIX machine. This is done by reading bytes bit by bit, converting them to floating-point numbers, then writing results to another file. Useful when data files created by VAX computer must be used on other machines. Written in C language.

Alves, Marcos↗
NASA NTRS1999

Programmable Logic Application Notes

This column will be provided each quarter as a source for reliability, radiation results, NASA capabilities, and other information on programmable logic devices and related applications. This quarter the focus is on some experimental data on low voltage drop out regulators to support mixed 5 and 3.3 volt systems. A discussion of the Small Explorer WIRE spacecraft will also be given. Lastly, we show take a first look at robust state machines in Hardware Description Languages (VHDL) and their use in critical systems. If you have information that you would like to submit or an area you would like discussed or researched, please give me a call or e-mail.

Katz, Richard↗