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DOE OSTI2023

Strengthening the US Department of Energy’s Recruitment Pipeline: The DOE/NNSA Predictive Science Academic Alliance Program (PSAAP) Experience

The US Department of Energy (DOE) oversees a system of 17 national laboratories responsible for developing unique scientific capabilities beyond the scope of academic and industrial institutions. These labs strive to keep America at the forefront of discovery and are home to some of the Nation’s best minds and the world’s best scientific and research facilities. Collaborations between national laboratories and academic institutions are critical to develop and recruit talent for the DOE workforce. Academia’s cooperative education model poses challenges for DOE recruitment pipelines centered around traditional internships. This paper discusses a promising DOE recruitment pipeline, the National Nuclear Security Administration’s (NNSA) Predictive Science Academic Alliance Program (PSAAP) initiative. As a part of this, experiences capturing the successes and challenges faced by the University of Utah’s Carbon Capture Multidisciplinary Simulation Center (CCMSC) through their participation in the PSAAP-II initiative are shared. These experiences demonstrate the success of Utah’s PSAAP center as a recruitment pipeline with approximately 43% of CCMSC students going to a national laboratory after graduation. Potential opportunities to strengthen the DOE’s recruitment pipeline are also discussed.

Holmen, John↗

DOE OSTI2023

FORGE: Pre-Training Open Foundation Models for Science

Large language models (LLMs) are poised to revolutionize the way we conduct scientific research. However, both model complexity and pre-training cost are impeding effective adoption for the wider science community. Identifying suitable scientific use cases, finding the optimal balance between model and data sizes, and scaling up model training are among the most pressing issues that need to be addressed. In this study, we provide practical solutions for building and using LLM-based foundation models targeting scientific research use cases. We present an end-to-end examination of the effectiveness of LLMs in scientific research, including their scaling behavior and computational requirements on Frontier, the first Exascale supercomputer. We have also developed for release to the scientific community a suite of open foundation models called FORGE with up to 26B parameters using 257B tokens from over 200M scientific articles, with performance either on par or superior to other state-of-the-art comparable models. We have demonstrated the use and effectiveness of FORGE on scientific downstream tasks. Our research establishes best practices that can be applied across various fields to take advantage of LLMs for scientific discovery.

Yin, Junqi↗

DOE OSTI2025

The (R)evolution of Scientific Workflows in the Agentic AI Era: Towards Autonomous Science

Modern scientific discovery increasingly requires coordinating distributed facilities and heterogeneous resources, forcing researchers to act as manual workflow coordinators rather than scientists. Advances in AI leading to AI agents show exciting new opportunities that can accelerate scientific discovery by providing intelligence as a component in the ecosystem. However, it is unclear how this new capability would materialize and integrate in the real world. To address this, we propose a conceptual framework where workflows evolve along two dimensions which are intelligence (from static to intelligent) and composition (from single to swarm) to chart an evolutionary path from current workflow management systems to fully autonomous scientific laboratories. With these trajectories in mind, we present an architectural blueprint that can help the community take the next steps towards harnessing the opportunities in autonomous science with the potential for 100x discovery acceleration and transformational scientific workflows.

Shin, Woong [ORNL] (ORCID:0000000172077814)↗

DOE OSTI2020

Mesoscale Science Data Analytics

This is software that will be used to do data analytics in experimental workflows for x-ray mesoscale science. This tool set will provide a mechanism for supporting experiments in many ways, from collecting calibration information and raw data, to managing and viewing data to extracting crystallographic and physical parameters. It will eventually include development of a fully automated workflow that will include statistical information and prediction capabilities to support the scientists in decision-making and replanning their experiments when necessary.

Sweeney, Christine↗

DOE OSTI2022

LANL Contributions to pyiron: An integrated development environment for computational materials science

To support and accelerate the development of simulation protocols in atomistic modelling, we introduce an integrated development environment (IDE) for computational materials science called pyiron (http://pyiron.org). The pyiron IDE combines a web based source code editor, a job management system for build automation, and a hierarchical data management solution. The core components of the pyiron IDE are pyiron objects based on an abstract class, which links application structures such as atomistic structures, projects, jobs, simulation protocols and computing resources with persistent storage and an interactive user environment. The simulation protocols within the pyiron IDE are constructed using the Python programming language. To highlight key concepts of this tool as well as to demonstrate its ability to simplify the implementation and testing of simulation protocols we discuss two applications. In these examples we show how pyiron supports the whole life cycle of a typical simulation, seamlessly combines ab initio with empirical potential calculations, and how complex feedback loops can be implemented. While originally developed with focus on ab initio thermodynamics simulations, the concepts and implementation of pyiron are general thus allowing to employ it for a wide range of simulation topics.

Janssen, Jan↗

DOE OSTI2022

GridDS: Data Science Toolkit for Energy Grid Data

According to the U.S. Energy Information Administration (EIA), the demand for energy is expected to increase 50% by the year 20501. While energy standards, such as the Institute of Electrical and Electronics Engineers (IEEE) Standard 1547, (Basso 2015) and monitoring with wide area management systems (WAMS) (Liu 2017, Zhou 2016) have enabled large scale data collection and storage, the application of this data in mitigating costs associated with increased consumer demand is an ongoing focus for energy research. This ubiquitous data collection presents a promising opportunity for machine learning and data science to improve efficiency of distributed energy resources (DERs). The GridDS software toolkit is designed to leverage advanced metering infrastructure (AMI), outage management systems data (OMS), Supervisory control Data Acquisition (SCADA), and geographic information systems (GIS) to forecast future energy demands and detect incipient grid failures. GridDS is a python software library designed to be modular and generalizable to data recorded by DERs. In adapting to disparate datasets recorded by various WAMS, GridDS provides a range of unique functionality not presently implemented in current WAMS which have highly specific software infrastructure by design. GridDS functionality ranges from data specification and preparation, to training and validation for state of the art machine learning, to interactive data visualization. For data intake, GridDS combines: Pandera: a library for creating data specifications. TimeScaleDB: a postgresSQL database infrastructure for efficient storage of timeseries data. Dataset class: A custom dataset class / interface that ensures modularity between a range of synthetic and live recorded datasets. Is

Ladd, Alexander↗

DOE OSTI2024

CALMS: CONTEXT-AWARE LANGUAGE MODEL FOR SCIENCE

Context-Aware Language Model for Science (CALMS) is designed to assist scientists with instrument operations and complex experimentation. CALMS consists of a large-language model (LLM) augmented with context and tool use abilities. With the ability to retrieve relevant information from facility documentation, CALMS can answer simple questions on scientific capabilities and other operational procedures. With the ability to interface with software tools and experimental hardware, CALMS can operate scientific instruments in a conversational manner.

CHERUKARA, MATTHEW↗

DOE OSTI2024

Digital Twin for Chemical Science (DTCS) v0.01

Directly visualizing the trajectories of chemistry can unravel novel insights into the behavior of catalysts, gas phase reactions, photo-induced dynamics, and building blocks for quantum information processing. The ability of explicitly identifying, tracking, and tagging the exchange of matter, hence the annihilation and creation of new chemical species, can be best realized through a close coupling of theory and experiment. While the synchrotron-based characterization facilities propelled rapidly in its hardware, providing higher brightness, better resolution, and more precision, the software infrastructure is lagging. We developed DTCS (Digital Twin for Chemical Science) v.01, a central platform that faithfully mimics advanced instrumentations in Scientific User Facilities, by solving a variety of technical challenges in data acquisition, analysis, and model-driven interpretation. Rooted in physics and accelerated by AI, we validated this concept by direct comparison with precise experimental X-ray Photoelectron Spectroscopy (XPS) observations using a ubiquitous metal-water interfacial scenario, i.e., Ag/H2O as our main narrative. The DTCS v.01 input mirrors how the bench chemists work, with the output directly linked to the end station computer, thereby providing a user-friendly, knowledge-driven, and accessible user experience with mechanistic insights standardized in a way that are ready to be published, versioned, and transferred flexibly.

Qian, Jin↗

DOE OSTI2026

poppler-science

The “Poppler-science” software is a fork of the existing open-source Poppler project (https://poppler.freedesktop.org/) for converting PDF files to text. Modifications to the Poppler source code include (a) per-glyph optical character recognition (for correcting the non-standard font glyph remapping that is common in the scientific literature), (b) inference of text markup for commonly used scientific formatting (like superscripts and subscripts), (c) table and figure recognition, and (d) improved ordering of text output for complex scientific manuscripts (e.g., multi-column text, figure and table captions, etc.).

Gans, Jason [Los Alamos National Laboratory]↗

DOE OSTI2021

The Exascale Framework for High Fidelity coupled Simulations (EFFIS): Enabling whole device modeling in fusion science

We present the Exascale Framework for High Fidelity coupled Simulations (EFFIS), a workflow and code coupling framework developed as part of the Whole Device Modeling Application (WDMApp) in the Exascale Computing Project. EFFIS consists of a library, command line utilities, and a collection of run-time daemons. Together, these software products enable users to easily compose and execute workflows that include: strong or weak coupling, in situ (or offline) analysis/visualization/monitoring, command-and-control actions, remote dashboard integration, and more. We describe WDMApp physics coupling cases and computer science requirements that motivate the design of the EFFIS framework. Furthermore, we explain the essential enabling technology that EFFIS leverages: ADIOS for performant data movement, PerfStubs/TAU for performance monitoring, and an advanced COUPLER for transforming coupling data from its native format to the representation needed by another application. Finally, we demonstrate EFFIS using coupled multi-simulation WDMApp workflows and exemplify how the framework supports the project’s needs. We show that EFFIS and its associated services for data movement, visualization, and performance collection does not introduce appreciable overhead to the WDMApp workflow and that the resource-dominant application’s idle time while waiting for data is minimal.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2024

Ginkgo - A math library designed to accelerate Exascale Computing Project science applications

Large-scale simulations require efficient computation across the entire computing hierarchy. A challenge of the Exascale Computing Project (ECP) was to reconcile highly heterogeneous hardware with the myriad of applications that were required to run on these supercomputers. Mathematical software forms the backbone of almost all scientific applications, providing efficient abstractions and operations that are crucial to harness the performance of computing systems. Ginkgo is one such mathematical software library, nurtured by ECP, providing high-performance, user-friendly, and performance portable interfaces for applications in ECP and beyond. In this paper, we elaborate on Ginkgo’s philosophy of high-performance software that is sustainable, reproducible, and easy to use. We showcase the wide feature set of solvers and preconditioners available in Ginkgo and the central concepts involved in their design. We elaborate on four different ECP software integrations: MFEM, PeleLM + SUNDIALS, XGC, and ExaSGD that use Ginkgo to accelerate their science runs. Performance studies of different problems from these applications highlight the effectiveness of Ginkgo and the benefits incurred by these ECP applications.

Cojean, Terry↗

DOE OSTI2022

Coupled Cluster Downfolding Theory: towards universal many-body algorithms for dimensionality reduction of composite quantum systems in chemistry and materials science

Abstract The recently introduced coupled cluster (CC) downfolding techniques for reducing the dimensionality of quantum many-body problems recast the CC formalism in the form of the renormalization procedure allowing, for the construction of effective (or downfolded) Hamiltonians in small-dimensionality sub-space, usually identified with the so-called active space, of the entire Hilbert space. The resulting downfolded Hamiltonians integrate out the external (out-of-active-space) Fermionic degrees of freedom from the internal (in-the-active-space) parameters of the wave function, which can be determined as components of the eigenvectors of the downfolded Hamiltonians in the active space. This paper will discuss the extension of non-Hermitian (associated with standard CC formulations) and Hermitian (associated with the unitary CC approaches) downfolding formulations to composite quantum systems commonly encountered in materials science and chemistry. The non-Hermitian formulation can provide a platform for developing local CC approaches, while the Hermitian one can serve as an ideal foundation for developing various quantum computing applications based on the limited quantum resources. We also discuss the algorithm for extracting the semi-analytical form of the inter-electron interactions in the active spaces.

Bauman, Nicholas P.↗

DOE OSTI2024

Constraints On Covariant WIMP-Nucleon Effective Field Theory Interactions from the First Science Run of the LUX-ZEPLIN Experiment

The first science run of the LUX-ZEPLIN (LZ) experiment, a dual-phase xenon time project chamber operating in the Sanford Underground Research Facility in South Dakota, USA, has reported leading limits on spin-independent WIMP-nucleon interactions and interactions described from a non-relativistic effective field theory (NREFT). Using the same 5.5~t fiducial mass and 60 live days of exposure we report on the results of a relativistic extension to the NREFT. We present constraints on couplings from covariant interactions arising from the coupling of vector, axial currents, and electric dipole moments of the nucleon to the magnetic and electric dipole moments of the WIMP which cannot be described by recasting previous results described by an NREFT. Using a profile-likelihood ratio analysis, in an energy region between 0~keV_{nr} to 270~keV_{nr}, we report 90% confidence level exclusion limits on the coupling strength of five interactions in both the isoscalar and isovector bases. .

Source record↗

DOE OSTI2025

AmeriFlux US-AMS Argonne Testbed for Multiscale Observational Science (ATMOS)

This is the AmeriFlux version of the carbon flux data for the site US-AMS Argonne Testbed for Multiscale Observational Science (ATMOS). Site Description - This tower is located at Argonne National Laboratory approximately 2 km north of the Des Plaines river and 115 m NNW from the ATMOS meteorological tower. It is downwind of a seasonally wet grassland bordered by forest 40 m N of the flux tower. The area was managed by controlled burns until 2019.

McNicol, Gavin [University of Illinois at Chicago]↗

DOE OSTI2021

News and Notes: SSA Task Force on Diversity, Equity, and Inclusion: Toward a Changing, Inclusive Future in Earthquake Science

In the United States, a wide variety of studies show that the geoscience community does not reflect the broader societal makeup (e.g., Velasco and Jaurrieta de Velasco, 2010; Dutt, 2020; Howley, 2020). In fact, only about 10% of all Science, Technology, Engineering, Mathematics (STEM) Ph.D. degrees are awarded to people of color, although they represent more than a third of the population (Dutt, 2020). These numbers have changed little over the past 40 years (e.g., Bernard and Cooperdock, 2018; Dutt, 2020). Recent events in the United States have again raised awareness of this discrepancy in many fields; similar divergences may be present throughout the world. How this discrepancy represents a tremendous loss of talent and contributes to ongoing bias and racism is discussed.

58 GEOSCIENCES↗

DOE OSTI2019

Proposal for a Fusion Prototype Neutron Source at the Los Alamos Neutron Science Center

This study evaluates a target station at the Los Alamos Neutron Science Center dedicated to the mission of irradiating structural materials in an environment that is nearly prototypic of the radiation environment experienced by the first wall of a magnetically-confined fusion reactor burning D-T fuel. Such a station could be located in the existing Area A experimental hall and be driven by an 800-MeV, 1-MW proton beam delivered by the LANSCE accelerator. A neutronics assessment of a target concept shows it satisfies all of the key parameter guidelines stated in the Summary Report on the Fusion Prototypic Neutron Source Workshop.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Science Opportunities and Capabilities Enabled by LCLS-II and LCLS-II HE

X-Ray Science with LCLS-II and LCLS-II HE: Chemical dynamics: Reaction dynamics, charge transfer, molecular photocatalysts, natural & artificial photosynthesis; Catalysis: Homogeneous and heterogeneous catalysis, interfacial & geo/environmental chemistry; Materials Physics: Heterogeneity, spontaneous fluctuations, nonequilibrium dynamics, extreme environments; Quantum Materials: Emergent phenomena & collective excitations; Biological Function & Structural Dynamics: Dynamics in physiological environments.

43 PARTICLE ACCELERATORS↗

DOE OSTI2020

Science-Driven Data Management for Multi-Tiered Storage (Final Report)

Scientific discovery at the exascale will not be possible without significant new research in the management, storage and retrieval over the long lifespan of the extreme amounts of data that will be produced. Our thesis is that adding application level knowledge about data to guide the actions of the storage system provides substantial benefits to the organization, storage, and access to extreme scale data, resulting in improved productivity for computational science. In this project we will demonstrate novel techniques to facilitate efficient mapping of data objects, even partitioning individual variables, from the user space onto multiple storage tiers, and enable application-guided data reductions and transformations to address capacity and bandwidth bottlenecks. Our goal is to address the associated Input/ Output (I/O) and storage challenges in the context of current and emerging storage landscapes, and expedite insights into mission critical scientific processes.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗