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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Building an Integrated Ecosystem of Computational and Observational Facilities to Accelerate Scientific Discovery

Future scientific discoveries will rely on flexible ecosystems that incorporate modern scientific instruments, high performance computing resources, parallel distributed data storage, and performant networks across multiple, independent facilities. In addition to connecting physical resources, such an ecosystem presents many challenges in logistics and accessibility, especially in orchestrating computations and experiments that span across leadership computing systems and experimental instruments. Past efforts have typically been application-specific or limited to interfaces for computing resources. This paper proposes a general framework for integrating computation resources and instrument operations, addressing challenges in code development/execution, data staging and collection, software stack, control mechanisms, resource authorization and governance, and hardware integration. We also describe a demonstration use case wherein a Bayesian optimization algorithm running on an edge computing resource guides a scanning probe microscope to autonomously and intelligently characterize a material sample. This science edge ecosystem framework will provide a blueprint for federating multi-institutional, disparate resources and orchestrating scientific workflows across them to enable next-generation discoveries.

Somnath, Suhas↗
DOE OSTI2025

Secure API-Driven Research Automation to Accelerate Scientific Discovery

The Secure Scientific Service Mesh (S3M) provides API-driven infrastructure to accelerate scientific discovery through automated research workflows. By integrating near real-time streaming capabilities, intelligent workflow orchestration, and fine-grained authorization within a service mesh architecture, S3M enables secure and flexible programmatic access to high performance computing (HPC) resources. This framework allows intelligent agents and experimental facilities to dynamically provision resources and execute complex workflows, accelerating experimental lifecycles, and enabling AI-augmented autonomous science. S3M establishes a modern foundation for scientific computing infrastructure that significantly reduces traditional barriers between researchers, computational resources, and experimental facilities.

Skluzacek, Tyler [ORNL] (ORCID:0000000322424931)↗
DOE OSTI2023

Accelerating scientific discoveries through data-driven innovations

Developing artificial intelligence (AI) and machine learning (ML) methods that can accelerate scientific discoveries and advance science has become one of the important research directions for the AI/ML research community. It has been gaining increasing attention from researchers in diverse scientific areas, including biomedical science, materials science, climate science, physics, chemistry, and many others. Data-driven AI/ML innovations to enable reliable predictions and optimal decision making for scientific discoveries face several critical challenges, among which are high system complexity, large search space, incomplete knowledge, and small data, all of which demand novel strategies to effectively address them. Meeting these challenges and thereby accelerating scientific discoveries and industrial innovations, calls for research that can take full advantage of the latest advances in AI/ML to integrate data-driven techniques with scientific knowledge and is able to execute them in modern high-performance computing (HPC) environments at scale. This Patterns special collection "Accelerating scientific discoveries through data-driven innovations" features articles that showcase the promising roles of AI/ML and data-driven modeling in accelerating scientific discoveries and may inspire the next wave of data-driven innovations in various scientific domains.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Scientific discovery in a model-centric framework: Reproducibility, innovation, and epistemic diversity

Consistent confirmations obtained independently of each other lend credibility to a scientific result. We refer to results satisfying this consistency as reproducible and assume that reproducibility is a desirable property of scientific discovery. Yet seemingly science also progresses despite irreproducible results, indicating that the relationship between reproducibility and other desirable properties of scientific discovery is not well understood. These properties include early discovery of truth, persistence on truth once it is discovered, and time spent on truth in a long-term scientific inquiry. We build a mathematical model of scientific discovery that presents a viable framework to study its desirable properties including reproducibility. In this framework, we assume that scientists adopt a model-centric approach to discover the true model generating data in a stochastic process of scientific discovery. We analyze the properties of this process using Markov chain theory, Monte Carlo methods, and agent-based modeling. We show that the scientific process may not converge to truth even if scientific results are reproducible and that irreproducible results do not necessarily imply untrue results. The proportion of different research strategies represented in the scientific population, scientists’ choice of methodology, the complexity of truth, and the strength of signal contribute to this counter-intuitive finding. Important insights include that innovative research speeds up the discovery of scientific truth by facilitating the exploration of model space and epistemic diversity optimizes across desirable properties of scientific discovery.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

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

In January 2019, the U.S. Department of Energy, Office of Science program in Advanced Scientific Computing Research, convened a workshop to identify priority research directions 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 system, cooperating toward a common objective. The workshop revealed two primary, interdependent motivations for processing and managing data in situ. The first motivation is that the in situ methodology enables scientific discovery from a broad range of data sources over a wide scale of computing platforms: leadership-class systems, clusters, clouds, workstations, and embedded devices at the edge. The successful development of ISDM capabilities will benefit real-time decision-making, design optimization, and data-driven scientific discovery. The second motivation is the need to decrease data volumes. ISDM can make critical contributions to managing large data volumes from computations and experiments to minimize data movement, save storage space, and boost resource efficiency, often while simultaneously increasing scientific precision. A fundamental finding of this workshop is that the methodologies used to manage data among a variety of tasks in situ can be used to facilitate scientific discovery from many different data sources—simulation, experiment, and sensors, for example—and that being able to do so at numerous computing scales will benefit real-time decision-making, design optimization, and data-driven scientific discovery across the Office of Science mission space. Applications wanting to use the in situ capabilities include those where data analysis feeds back to the simulation, decisions are made autonomously, big data or machine learning is among the tasks to be coordinated, and computations need to be completed in real time. The workshop identified six priority research directions that highlight the components and capabilities needed for ISDM to be successful for the wide variety of applications discussed: making ISDM capabilities more pervasive, controllable, composable, and transparent, with a focus on greater coordination with the software stack and a diversity of fundamentally new data algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Priority research directions for in situ data management: Enabling scientific discovery from diverse data sources

In January 2019, the US Department of Energy, Office of Science program in Advanced Scientific Computing Research, convened a workshop to identify priority research directions (PRDs) for in situ data management (ISDM). A fundamental finding of this workshop is that the methodologies used to manage data among a variety of tasks in situ can be used to facilitate scientific discovery from many different data sources—simulation, experiment, and sensors, for example—and that being able to do so at numerous computing scales will benefit real-time decision-making, design optimization, and data-driven scientific discovery. This article describes six PRDs identified by the workshop, which highlight the components and capabilities needed for ISDM to be successful for a wide variety of applications—making ISDM capabilities more pervasive, controllable, composable, and transparent, with a focus on greater coordination with the software stack and a diversity of fundamentally new data algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Report for the ASCR Workshop on Visualization for Scientific Discovery, Decision-Making, and Communication

Visualization—the use of visual elements to explore data, form hypotheses, or convey conclusions—is an integral part of the scientific process. Starting from an initial exploration of new data to illustrating outcomes for the general public, visualization is one of the most intuitive and powerful modes of communication. With the explosion of new data sources and types, unprecedented volumes of data, and new technologies, such as virtual reality (VR) and artificial intelligence (AI), visualization has become increasingly essential but also ever more challenging. The Department of Energy’s (DOE) Office of Advanced Scientific Computing Research (ASCR) sponsored a Basic Research Needs workshop in January 2022 to understand the major opportunities and grand challenges in visualization tools and technologies for scientific computing as well as for DOE-relevant applications and goals in general. The workshop identified five priority research directions (PRDs) for visualization to support scientific discovery, decision making, and communication. The first three PRDs describe interconnected research themes addressing the need for new techniques to deal with complex data, uncertainty, and interpretability (PRD 1); the need for scalable and interoperable software stacks (PRD 2); and the challenges and opportunities inherent in new technologies, such as VR, cloud, or exascale computing (PRD 3). The remaining two PRDs describe foundational research themes that recognize the potential of visualizations to provide equitable access to information and to strengthen the scientific discourse (PRD 4); and the need to consider human factors when designing visualizations (PRD 5). Collectively, these PRDs form the pillars for a coherent, long-term research and development strategy in Visualization for Scientific Discovery, Decision-Making, and Communication in the context of the Office of Science’s mission scope.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Building MCP-native hierarchical AI scientist ecosystems: a perspective on scaling multi-agent scientific discovery

Large language models (LLMs) are evolving from chatbots with limited tool-using capabilities to agentic AI systems that can perform deep research, assist in proposing hypotheses, help design experiments, automate data analysis, and draft scientific reports. However, there are currently two bottlenecks limiting LLMs' real-world impact on the broader scientific research community beyond academic demonstrations: lack of interoperability (repetitive manual tool-integration is required across scenarios) and the need for scalable coordination (unstructured communication and memory become brittle as the number of agents grows). In this Perspective, we argue that the next phase of agentic scientific discovery requires the development of an ecosystem of protocol-native agents and tools organized through hierarchies inspired by human society, beyond the current paradigm of a single monolithic “AI scientist”. We use Model Context Protocol (MCP) as a concrete example of an emerging interoperability layer for scientific tool and context exchange, and we propose three complementary pathways to increase the scaling capabilities of an MCP-native scientific ecosystem by addressing the composability issues: (1) MCP servers for high-value scientific tools maintained by domain experts, (2) automated transformation of existing code repositories into MCP services, and (3) autonomous invention and evolution of new agents and workflows. Finally, we provide a practical roadmap for scaling AI-driven scientific discovery by expanding tool supply and coordination in MCP-native scientific ecosystems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Harness the Power of AI and CI/CD to Fuel Scientific Discovery

The "Harness the Power of AI and CI/CD to Fuel Scientific Discovery" project aims to enhance and automate critical scientific computing systems used in large-scale experiments like CMS at LHC and DUNE at Fermilab. By leveraging GlideinWMS and HEPCloud, this initiative focuses on developing containerized CI/CD pipelines, integrating AI for code quality improvement, and automating security verifications. Participants will gain hands-on experience with distributed computing systems and implement secure communications, contributing to real-world scientific progress and the open-source community.

Nurcellari, Tea↗
DOE OSTI2020

Algorithms and Statistical Models for Scientific Discovery in the Petabyte Era

The field of astronomy has arrived at a turning point in terms of size and complexity of both datasets and scientific collaboration. Commensurately, algorithms and statistical models have begun to adapt --- e.g., via the onset of artificial intelligence --- which itself presents new challenges and opportunities for growth. This white paper aims to offer guidance and ideas for how we can evolve our technical and collaborative frameworks to promote efficient algorithmic development and take advantage of opportunities for scientific discovery in the petabyte era. We discuss challenges for discovery in large and complex data sets; challenges and requirements for the next stage of development of statistical methodologies and algorithmic tool sets; how we might change our paradigms of collaboration and education; and the ethical implications of scientists' contributions to widely applicable algorithms and computational modeling. We start with six distinct recommendations that are supported by the commentary following them. This white paper is related to a larger corpus of effort that has taken place within and around the Petabytes to Science Workshops (https://petabytestoscience.github.io/).

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Visualization for Scientific Discovery, Decision-Making, and Communication

Visualization–the use of visual elements to explore data, form hypotheses, or convey conclusions–is an integral part of the scientific process. Starting from an initial exploration of new data to illustrating outcomes to the general public, visualization is one of the most intuitive and powerful modes of communication. With the explosion of new data sources and types, unprecedented volumes of data, and new technologies, such as virtual reality and AI, visualization has become increasingly essential but also ever more challenging. Department of Energy’s (DOE) Office of Advanced Scientific Computing Research (ASCR) sponsored a Basic Research Needs workshop in January 2022 to understand the major opportunities and grand challenges in visualization tools and technologies for scientific computing, with a special focus on DOE-relevant applications and goals. The workshop identified five priority research directions (PRDs) for visualization to support scientific discovery, decision-making, and communication.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Deep Learning for Scientific Discovery

Deep learning has positively impacted fields were perceptive tasks are paramount, such as computer vision and natural language processing. Tremendous strides have also been made in deep reinforcement learning towards robotics and gaming. Given the success in these fields, it is apparent that deep learning may also accelerate scientific discovery. Herein, we discuss several topic areas where modern representation learning is driving innovation. This article presents a) learning non-linear models from data, b) learning scientific laws from models, c) deep learning on graphs, and d) approaches to scalable deep learning.

deep learning, scientific computing, koopman, mate↗
DOE OSTI2018

The EGS Data Collaboration Platform: Enabling Scientific Discovery

Collaboration in the digital age has been stifled in recent years. Reasonable responses to legitimate security concerns have created a virtual landscape of silos and fortified castles incapable of sharing information efficiently. This trend is unfortunately opposed to the geothermal scientific community's migration toward larger, more collaborative projects. To facilitate efficient sharing of information between team members from multiple national labs, universities, and private organizations, the 'EGS Collab' team has developed a universally accessible, secure data collaboration platform and has fully integrated it with the U.S. Department of Energy's (DOE) Geothermal Data Repository (GDR) and the National Geothermal Data System (NGDS). This paper will explore some of the challenges of collaboration in the modern digital age, highlight strategies for active data management, and discuss the integration of the EGS Collab data management platform with the GDR to enable scientific discovery through the timely dissemination of information.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A Grassroots Network and Community Roadmap for Interconnected Autonomous Science Laboratories for Accelerated Discovery

Scientific discovery is being revolutionized by AI and autonomous systems, yet current autonomous laboratories remain isolated islands unable to collaborate across institutions. We present the Autonomous Interconnected Science Lab Ecosystem (AISLE), a grassroots network transforming fragmented capabilities into a unified system that shorten the path from ideation to innovation to impact and accelerates discovery from decades to months. AISLE addresses five critical dimensions: (1) cross-institutional equipment orchestration, (2) intelligent data management with FAIR compliance, (3) AI-agent driven orchestration grounded in scientific principles, (4) interoperable agent communication interfaces, and (5) AI/ML-integrated scientific education. By connecting autonomous agents across institutional boundaries, autonomous science can unlock research spaces inaccessible to traditional approaches while democratizing cutting-edge technologies. This paradigm shift toward collaborative autonomous science promises breakthroughs in sustainable energy, materials development, and public health.

Ferreira da Silva, Rafael [Oak Ridge National Labo↗
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↗
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↗