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DOE OSTI2023

Advanced Research Directions on AI for Science, Energy, and Security: Report on Summer 2022 Workshops

Over the past decade, fundamental changes in artificial intelligence (AI)—from foundational to applied—have delivered dramatic insights across a wide breadth of U.S. Department of Energy (DOE) mission space. AI is helping to augment and improve scientific and engineering workflows (e.g., for control, design, and dramatic performance gains through surrogate models) in national security, the Office of Science, and DOE’s applied energy programs. The progress and potential for AI in DOE science was captured in the 2020 “AI for Science” report from the DOE laboratory community in collaboration with academia and industry. Specific scientific areas ready to further leverage the power of AI ranged from the scale and performance of computational models to data analysis to creating new classes of observations using computer vision. Since that report, the scale and scope of scientific AI have accelerated, revealing new, emergent properties that yield insights that go beyond enabling opportunities to being potentially transformative in the way that scientific problems are posed and solved. Thus, under the guidance of both the Office of Science (SC) and the National Nuclear Security Administration (NNSA), the DOE national laboratories organized a series of workshops in 2022 to gather input on new and rapidly emerging opportunities and challenges of scientific AI. This 2023 report is a synthesis of those workshops. The scientific community believes AI can have a foundational impact on a broad range of DOE missions, including science, energy, and national security. Further, DOE has unique capabilities that enable the community to drive progress in scientific use of AI, building on long-standing DOE strengths and investments in computation, data, and communications infrastructure, spanning the Energy Sciences Network (ESnet), the Exascale Computing Project (ECP), and integrative programs such as the NNSA Office of Defense Programs Advanced Simulation and Computing (ASC) and the SC Scientific Discovery through Advanced Computing (SciDAC) programs.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

High gradient research and facilities at Los Alamos National Laboratory [Slides]

A particle accelerator is a scientific instrument that produces a directional stream of electrically charged particles, usually electrons or protons. The accelerator propels particles to high speeds, close to the speed of light. Historically, accelerators play a crucial role in scientific discovery. Nowadays, accelerators produce particle beams for biomedical and material research, manufacturing, energy technology, and Homeland security.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Powering the Future: Fusion & Plasmas

This report provides a decade-long vision for the field of fusion energy and plasma science and presents a path to a promising future of new scientific discoveries, industrial applications, and, ultimately, the delivery of fusion energy. We identify critical areas for research and development and prioritize investments to maximize impact. The research community worked for more than a year to develop a wealth of creative ideas designed to accelerate fusion energy and advance plasma science. The effort culminated in the consensus Community Planning Process report. Our work is based heavily on that report, and we extend our sincere gratitude to our colleagues for their efforts. Following the research community’s lead, we worked through consensus to generate this report. Many ideas were heard and were thoughtfully deliberated until a shared view on each issue emerged. This process allowed us to discuss and appreciate our different points of view and come to consensus language. Ultimately, we speak with one voice in conveying a vision for a vibrant program that will bring significant benefit to society.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

The SciDAC QuantOm Framework: A composable Workflow

As part of the Scientific Discovery through Advanced Computing (SciDAC) program, the Quantum Chromodynamics Nuclear Tomography (QuantOM) project aims to analyze data from Deep Inelastic Scattering (DIS) experiments conducted at Jefferson Lab and the upcoming Electron Ion Collider. The DIS data analysis is performed on an event-level by leveraging nuclear theory models and accounting for experimental conditions. In order to efficiently run multiple analyses under varying conditions, a composable workflow was designed where each section (theory, experiment, objective minimization, etc.) has its own dedicated module. The optimization, i.e. the fit of theory to experimental data is carried out by deep learning techniques, such as Generative Adversarial Networks (GANs) or Reinforcement Learning (RL). This presentation gives an overview of the current status of the workflow, highlights present and future challenges, and highlights possible extensions to other projects with similar requirements.

Lersch, Daniel↗
DOE OSTI2023

High Performance Computing Facility Operational Assessment 2022: Oak Ridge Leadership Computing Facility

The Oak Ridge Leadership Computing Facility (OLCF) was established to accelerate scientific discovery by providing world-leading computational performance and advanced data infrastructure. As a US Department of Energy (DOE) Office of Science user facility, the OLCF has managed the successful deployment and operation of a succession of leadership-class resources dedicated to open science. In addition to these resources, the OLCF staff continually strive to develop innovative processes and technologies, improve security, and empower users through allocation management and comprehensive user support and training. These efforts support the advancement of science by the OLCF users and benefit high-performance computing (HPC) facilities around the world. In calendar year (CY) 2022, the OLCF supported 1,681 users and 570 projects and exceeded all targets for user satisfaction. The facility received an average satisfaction score of 4.6 out of 5 on the annual user survey, and 96% of respondents reported a high satisfaction rate with the OLCF overall. Of the 3,212 user tickets submitted in CY 2022, OLCF staff resolved 97% within 3 business days. The facility also introduced several new services for users this year, including weekly virtual office hours with subject matter experts from ORNL and vendor partners; new views in MyOLCF that allow users to analyze allocation and compute usage for a project; the ability to build and run containers on Summit; and improved data visualization support and training resources.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Project Assessment for Biological and Environmental Research: Report from the BER Advisory Committee

The construction, operation, and stewardship of large-scale scientific user facilities and cutting edge capabilities have been integral to the mission of the U.S. Department of Energy (DOE) Office of Science from its earliest days. To help identify and prioritize new or upgraded facilities critical to scientific innovation over the next 10 years, the Office of Science director issued a charge to the federal advisory committees of six of its program offices in December 2023, including the Biological and Environmental Research (BER) program. The charge letter (see p. ii) asked the advisory committees to: 1. Consider what new or upgraded facilities will be necessary to position the Office of Science at the forefront of scientific discovery. 2. Deliver a short letter report describing each facility in terms of two criteria: (a) the potential to contribute to world-leading science in the next decade and (b) the readiness for construction.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Scaling the SciDAC QuantOm Workflow

As part of the Scientific Discovery through Advanced Computing (SciDAC) program, the Quantum Chromodynamics Nuclear Tomography (QuantOM) project aims to analyze data from Deep Inelastic Scattering (DIS) experiments conducted at Jefferson Lab and the upcoming Electron Ion Collider. The DIS data analysis is performed on an event-level by combining the input from theoretical and experimental nuclear physics into a single, composable workflow. The optimization itself (I.e. fitting the experimental data with theoretical predictions) is carried out by a machine / deep learning algorithm. The size of the acquired DIS data as well as the complexity of the workflow itself require that the analysis is performed across multiple GPUs on high performance computing systems, such as Polaris at Argonne National Laboratory. This presentation discusses the novelties and challenges that came along with parallelizing this workflow. Recent results are compared to common distributed training techniques.

Lersch, Daniel↗
DOE OSTI2024

Enabling Capabilities and Resources: 2024 Principal Investigator Meeting Proceedings

As a major supporter of basic genome-enabled research, BER’s Biological Systems Science Division (BSSD) fosters scientific discovery by funding - fundamental biological research across disciplines in conjunction with enabling investigational tools and computational capabilities that include world-class user facilities. The overarching goal of BSSD is to provide the necessary fundamental science to understand, predict, manipulate, and design biological systems that underpin innovations for bioenergy and bioproduct production and enhance understanding of natural, DOE-relevant environmental processes (Biological Systems Science Division Strategic Plan, 2021). To accelerate the U.S. bioeconomy, BSSD pursues innovative science underpinning advances in sustainable biofuels and bioproducts and the development of next-generation technologies and computational resources for systems biology research. The 2024 BSSD Enabling Capabilities and Resources (ECR) Principal Investigator (PI) meeting brought together PIs across the BSSD ECR portfolio to confer on shared interests and opportunities. The meeting was held concurrently with the Genomic Science program (GSP) PI meeting to optimize collaboration on research to advance bioenergy and the bioeconomy. Rick Stevens of Argonne National Laboratory gave a keynote on How Generative Artificial Intelligence Can Impact Biological Research (see Keynote: How Generative Artificial Intelligence Can Impact Biological Research, this page). Plenary presentations included several joint sessions that illuminated the integration and understanding of the larger BSSD mission. GSP’s objective is to provide systems-level understanding of plants, microbes, and their communities through its Bioenergy Research, Biosystems Design, and Environmental Microbiome Research portfolios. The objective of the ECR portfolio is to support development of computational and instrumental platforms to advance fundamental GSP research—and BER more broadly— toward the overall goal of understanding the functional principles of living systems and their response to environmental challenges.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Addressing Experimental Challenges in Probing Dark Energy with Accuracy and Precision with the Rubin Observatory Legacy Survey of Space & Time (LSST)

With support from this award, the Stanford group under the leadership of PI Patricia Burchat focused on pixel-level algorithms that are key to the investigation of dark energy with the Vera C. Rubin Observatory Legacy Survey of Space and Time (LSST). The work was carried out through strong engagement in the DOE-supported LSST Dark Energy Science Collaboration (DESC), for which the PI served as Deputy Spokesperson (07/2019 - 07/2021) and Technical Coordinator (07/2021 - 07/2023). Members of the Burchat group are Full DESC members and are among scientists who have been accepted to join the Rubin Observatory System Integration, Test, and Commissioning (SIT-Com) effort. The specific intellectual focus of the group was the optimization of gravitational lensing as a probe of the evolution of the distribution of dark matter in space and time to learn about the nature of dark energy. The Stanford group pursued an integrated research program to understand in detail, and then remediate or calibrate, systematic effects that could limit the sensitivity of the LSST to cosmic shear as a dark energy probe -- particularly those related to our understanding of the atmospheric point spread function (PSF), challenges associated with blended galaxy images, and impacts of residual camera artifacts. Key to this work was developing, enhancing, and validating the fidelity of simulations that play a key role in the development of our understanding of subtle systematic biases and then using these simulations to develop techniques to characterize and mitigate biases, and associated algorithms for implementation in the LSST or DESC science pipelines. The group’s work included contributions to the open-source GalSim simulation package, which is widely used in the community; simulations and studies of spatial correlations of atmospheric PSFs; studies of temporal correlations of the atmospheric PSF; impacts of chromatic PSFs on cosmic shear calibration; continued studies of anisotropic Gaussian process interpolation of PSF models across the focal plane; and measuring and mitigating impacts of residual camera artifacts on cosmic shear. A primary impact of this work is that it increases the scientific discovery potential of the DOE and NSF investments in the Rubin Observatory Legacy Survey of Space \& Time, by addressing challenges in interpreting astronomical images through more accurate descriptions of the blurring of images due to the atmosphere. Specifically, this work impacts our ability to understand the fundamental nature of dark energy.

Burchat, Patricia Rose↗
DOE OSTI2024

Aberration corrected RF flipper for high resolution neutron spectroscopy

Project Summary Company: Adelphi Technology, Inc. Title: Aberration-corrected High Frequency RF Flipper for High-Resolution Neutron Spectroscopy PI: Dr. Jay Theodore Cremer Topic: C55-11 Enhancement of Scattering Instrumentation Technology Used at Pulsed and Continuous Sources Subtopic: d. Other Statement of the problem or situation that is being addressed. The quest to understand heterogeneous and hierarchical materials is gathering momentum, as described in a 2015 report by the Basic Energy Sciences Advisory Committee on Challenges at the Frontiers of Matter and Energy. For the past 40 years a technique called neutron spin echo (NSE) has been used to probe molecular motions in such non-crystalline materials over time scales from 10’s of picoseconds to 100’s of nanoseconds. The method has provided unique information about the dynamics of soft heterogeneous materials, including confirmation of the de Gennes model of polymer reptation and quantitative measurement of bending constants of biologically relevant lipid membranes. However, scientists continue to clamor for even higher resolution than NSE can provide. Biomaterials, polymers, glasses, and artificially nanostructured materials all manifest slow molecular motions because of weak or competing interactions between subunits and are amenable to study with neutrons, provided sufficiently long dynamical correlation times can be achieved. All these materials have important applications to advanced technologies so understanding them is key to technological progress. General statement of how this problem is being addressed. We will address the need for high-resolution neutron spectroscopy by using a technique called Neutron Resonance Spin Echo (NRSE). While similar to NSE in many respects, this method has the potential to exceed the NSE capabilities, if 2 technical hurdles can be overcome. The major impediments to successful high-resolution NRSE are the availability of two technologies: a very high frequency, efficient, radiofrequency (rf) flipper for neutrons and a method to correct certain magnetic aberrations. Based on previous STTR support and follow-on research we have developed a suitable rf flipper and we have invented a method to correct the magnetic aberrations. Both technologies need refinement to make them suitable for implementation at a neutron source such as the Oak Ridge National Laboratory nuclear reactor. In this proposal we seek to perfect the two technologies and to combine them into a single, operationally convenient device. Commercial Applications and Other Benefits In view of the increasing demand for the unique scientific information that high resolution neutron spectroscopy can provide, we expect several major instrumentation upgrades at both U.S. and foreign neutron centers will require make use of the NRSE method over the coming decade, creating a market for the devices we will design. These components will enhance scientists’ abilities to probe the time dependence of density fluctuations in a wide range of hierarchical and heterogeneous materials many of which are vital to existing and future technologies. Key Words – Polarized Neutrons, Neutron Spin Echo, Neutron Scattering, advanced materials. Summary for Members of Congress Neutron beams are a powerful materials-science probe that provide unique information about the structure of matter. The proposed devices will accelerate scientific discoveries required to achieve national goals for new technological materials.

36 MATERIALS SCIENCE↗
DOE OSTI2024

A banner year for solar balloon technology

Solar-powered stratospheric balloons achieved major advancements in test flights, developmental milestones, and operational achievements this year. A solar-powered stratospheric balloon system known as the heliotrope, has emerged as a low-cost platform capable of carrying several pounds of scientific payload to altitudes ranging from 50,000 to 75,000 feet (Bowman et al., 2020; Image 1) for multi-hour level flights. This innovative system has facilitated groundbreaking scientific discoveries in areas such as infrasound and atmospheric composition. Unlike sealed weather balloons that burst, these balloons remain buoyant by retaining hot air within their open envelopes. Because they are solar-powered, they remain aloft as long as the sun remains unobscured.

14 SOLAR ENERGY↗
DOE OSTI2024

Revolutionizing Neuromorphic Computing for Science (Brochure on the 2024 ASCR Workshop on Neuromorphic Computing for Science)

The ASCR basic research needs for Neuromorphic Computing for Science workshop was held in September 2024. The workshop brochure and report aim to inform and draft a set of grand challenges for advancing the field of neuromorphic computing and developing proof of principle neuromorphic circuits applicable for High Performance Computer (HPC) acceleration for scientific discovery, and brainstorm ideas needed for a successful, robust, and world leading basic research program. The resulting priority research directions are: (1) Neuromorphic computing circuit primitives; (2) Connectivity, communication, and hardware integration; (3) Neuroscience-derived dynamics and algorithms; and (4) Ecosystem for scalable neuromorphic co-design. Breakthroughs in understanding, designing, and prototyping the circuitry and simulation capabilities for a truly neuromorphic computer are essential to enable progress in the field.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Revolutionizing Neuromorphic Computing for Science (Report for the 2024 ASCR Workshop on Neuromorphic Computing for Science)

The ASCR basic research needs for Neuromorphic Computing for Science workshop was held in September 2024. The workshop brochure and report aim to inform and draft a set of grand challenges for advancing the field of neuromorphic computing and developing proof of principle neuromorphic circuits applicable for High Performance Computer (HPC) acceleration for scientific discovery, and brainstorm ideas needed for a successful, robust, and world leading basic research program. The resulting priority research directions are: (1) Neuromorphic computing circuit primitives; (2) Connectivity, communication, and hardware integration; (3) Neuroscience-derived dynamics and algorithms; and (4) Ecosystem for scalable neuromorphic co-design. Breakthroughs in understanding, designing, and prototyping the circuitry and simulation capabilities for a truly neuromorphic computer are essential to enable progress in the field.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

"Forward" Projects Boost U.S. Leadership in Advanced Computing and Artificial Intelligence

High-performance computing (HPC) has been an indispensable research tool for accessing physical realms difficult, or impossible, achieve with experiment alone. For several decades, the Department of Energy’s (DOE’s) Office of Science has deployed sophisticated HPC systems for solving the nation’s most pressing grand challenge problems in energy, climate change, and human health. In addition, DOE’s National Nuclear Security Administration (NNSA) has adeptly applied HPC in support of key national security objectives, such as nuclear science and stockpile modernization and stewardship. Over time, HPC systems have become increasingly more complex and capable, and as each new machine has come online, scientists and engineers have taken advantage of vast increases in compute power to accelerate scientific discoveries and engineering innovation.

42 ENGINEERING↗
DOE OSTI2024

PCAST: Report to the President on Supercharging Research: Harnessing Artificial Intelligence to Meet Global Challenges

The President’s Council of Advisors on Science and Technology (PCAST) is the sole body of advisors from outside the federal government charged with making science, technology, and innovation policy recommendations to the President and the White House. Established by Executive Order, it is an independent Federal Advisory Committee comprised of distinguished individuals from industry, academia, and non-profit organizations with a range of perspectives and expertise. On April 29, 2024, PCAST sent a report to the President with recommendations to help the U.S. harness the full potential of AI to equitably and responsibly supercharge scientific discovery.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

AEOLUS: Advances in Experimental Design, Optimal Control, and Learning for Uncertain Complex Systems

Sustained advances in the mathematics of modeling and simulation have resulted in the capability today for routine simulation of a number of large scale complex DOE-relevant systems. As remarkable as this capability for solving the so-called forward problem is, it is typically only the first step-an inner loop within an outer loop that explores the simulation model's parameter space and decision space to characterize uncertainty in the model's predictions, learn unknown model parameters from data, design the most informative experiments, determine optimal control strategies, and create optimal designs. Broadly, what unifies all of these outer loop problems is that they are, in one form or another, optimization problems over parameter/control/design space that are constrained by complex uncertain models. To fully realize the power of scientific simulation as a basis for scientific discovery, technological innovation, and rational decision-making, it is imperative to move beyond simulation to tackle the outer loop of optimization for learning from data, experimental design, and control with complex uncertain models. When the models under consideration are large-scale and complex, and when the optimization variable and uncertain parameter spaces are high (or infinite) dimensional, this constitutes a grand challenge of the highest order, and is intractable with conventional methods. To overcome these challenges, the AEOLUS Center was established to develop a unified mathematical, computational, and statistical framework for (1) Learning predictive models from complex data via Bayesian inference and optimization, and (2) Optimizing experiments, processes, and designs using the resulting uncertain models. These problems are intractable with conventional methods, for several reasons: (1) The simulation problems that govern the inner loops of the optimization problems are expensive to execute (due to severe nonlinearity, heterogeneity, multiphysics/multiscale coupling); (2) The optimization variable and uncertain parameter spaces are high dimensional, often stemming from discretizations of infinite dimensional fields such as initial conditions, sources, or material properties. We argue that the key to overcoming these challenges is to develop new mathematical, computational, and statistical methods that exploit the structure of the Bayesian inference and optimization problems mediated by their underlying complex uncertain models. This structure includes the regularity, sparsity, geometry, low intrinsic dimensionality, and multifidelity nature of the maps from uncertain parameter/optimization variable spaces to the specific objectives targeted: Bayesian inference, optimal experimental design, and optimal control design. Black box methods developed as generic tools are incapable of exploiting this structure. To be successful, we must create, integrate, and cross-fertilize ideas across multiple areas of applied math--including approximation theory, Bayesian inference, data science, experimental design, information theory, machine learning, model reduction, optimal control theory, parallel algorithms, PDE-constrained optimization, randomized algorithms, stochastic optimization, and uncertainty quantification--all while exploiting the structure of the problems at hand. With this goal in mind, we have marshaled a team of leading authorities in these areas. While the methods we develop will be broadly applicable across a wide spectrum of DOE problems in which experiments inform models and the systems those models describe must be optimized under uncertainty, we have chosen a specific area, advanced manufacturing and materials, to drive our work. AMM is characterized by complex models across multiple scales, and is a rich source of challenging problems in inference, experimental design, and optimal control, requiring multifaceted and integrated advances in applied mathematics. As such, AMM serves as an excellent vehicle to motivate and demonstrate the advances in applied mathematics developed by our center.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Tokamak Disruption Simulation (Final Technical Report)

The Tokamak Disruption Simulation collaboration was a SciDAC (Scientific Discovery through Advanced Computing) program led by Xianzhu Tang of the Los Alamos National Laboratory but with separate grants to each institution. I was the PI for Columbia University and the only person at Columbia directly funded by this SciDAC. Disruptions are a sudden loss of confinement in tokamaks, and a reliable method of preventing disruptions must be developed before tokamak fusion power plants become feasible. A related plasma confinement concept, the stellarator, is the only way known way of preventing disruptions with the required reliability. The largest devices, both existing and under construction, for confining plasmas for fusion using magnetic fields are tokamaks, so it is important to solve their disruption issues. The sudden loss of confinement in tokamaks can be from a bit of material falling into the plasma, which causes the plasma energy to be quickly lost by radiation, or by an instability causing a breakup of the confining surfaces formed by the magnetic field. This breakup can occur more than a million times faster than one would naively think possible. The sudden breakup of surfaces is called a fast magnetic reconnection and also results in a sudden cooling of the plasma.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗