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At least 469 records · Page 26

Salton Sea Geodatabase for Geothermal Exploration Artificial Intelligence

These files contain the geodatabases related to Salton Sea Geothermal Field. It includes all input and output files used with the Geothermal Exploration Artificial Intelligence. Input and output files are sorted into three categories: raw data, pre-processed data, and analysis (post-processed data). In each of these categories there are six additional types of raster catalogs which are titled Radar, SWIR, Thermal, Geophysics, Geology, and Wells. The files are used with the Geothermal Exploration Artificial Intelligence for the Salton Sea Geothermal Site to identify indicators of blind geothermal systems. The included zip file is a geodatabase to be used with ArcGIS and the tar file is an inclusive database that encompasses the inputs and outputs for the Salton Sea Geothermal Site.

15 GEOTHERMAL ENERGY↗

AI-assisted object condensation clustering for calorimeter shower reconstruction at CLAS12

Several nuclear physics studies using the CLAS12 detector rely on the accurate reconstruction of neutrons and photons from its forward angle calorimeter system. These studies often place restrictive cuts when measuring neutral particles due to an overabundance of false clusters created by the existing calorimeter reconstruction software. In this work, we present a new AI approach to clustering CLAS12 calorimeter hits based on the object condensation framework. The model learns a latent representation of the full detector topology using GravNet layers, serving as the positional encoding for an event’s calorimeter hits which are processed by a Transformer encoder. This unique structure allows the model to contextualize local and long range information, improving its performance. Evaluated on one million simulated $e^-$ $+$ $p$ collision events, our method significantly improves cluster trustworthiness: the fraction of reliable neutron clusters, increasing from 8.88% to 30.73%, and photon clusters, increasing from 51.07% to 64.73%. In conclusion, our study also marks the first application of AI clustering techniques for hodoscopic detectors, showing potential for usage in many other experiments.

Calorimeters↗

Advanced Computing Annual Report 2024

In fiscal year (FY) 2024, the National Renewable Energy Laboratory (NREL) took a major leap forward with the completed full buildout of Kestrel, the Office of Energy Efficiency and Renewable Energy's newest high-performance computing (HPC) system. Kestrel is already supporting science across the portfolio, bringing roughly 44 petaflops of computing power, which is more than five times the capacity of our previous supercomputer, Eagle. By delivering greater GPU capacity, Kestrel enables faster progress in artificial intelligence (AI) and opens new avenues in energy research - from defining long-term planning scenarios to accommodate a growing power system to material discovery to improving energy efficiency in photovoltaics (PV). Across the portfolio, research is being accelerated by Kestrel's impressive power. During FY24, 427 projects and more than 700 researchers used NREL's HPC, supporting the U.S. Department of Energy's Office of Energy Efficiency and Renewable Energy across 13 funding areas. Through these collaborations, researchers produced more than 450 technical outputs, including 195 articles in peer-reviewed publications, pushing the boundaries of science and engineering. This year's report features new sections spotlighting the expanding roles of Artificial Intelligence and Accelerated Computing. We also introduce an early career section to celebrate the accomplishments of our up-and-coming researchers, whose pioneering work is shaping the future of energy. We hope you enjoy the new insights and discoveries highlighted in these pages.

97 MATHEMATICS AND COMPUTING↗

I ntera C hem : Exploring Excited States in Virtual Reality with Ab Initio Interactive Molecular Dynamics

InteraChem is an ab initio interactive molecular dynamics (AI-IMD) visualizer that leverages recent advances in virtual reality hardware and software, as well as the graphical processing unit (GPU)-accelerated TeraChem electronic structure package, in order to render quantum chemistry in real time. We introduce the exploration of electronically excited states via AI-IMD using the floating occupation molecular orbital-complete active space configuration interaction method. The optimization tools in InteraChem enable identification of excited state minima as well as minimum energy conical intersections for further characterization of excited state chemistry in small- to medium-sized systems. We demonstrate that finite-temperature Hartree–Fock theory is an efficient method to perform ground state AI-IMD. InteraChem allows users to track electronic properties such as molecular orbitals and bond order in real time, resulting in an interactive visualization tool that aids in the interpretation of excited state chemistry data and makes quantum chemistry more accessible for both research and educational purposes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

SDYN-GANs: Adversarial learning methods for multistep generative models for general order stochastic dynamics

We introduce adversarial learning methods for data-driven generative modeling of dynamics of nth-order stochastic systems. Our approach builds on Generative Adversarial Networks (GANs) with generative model classes based on stable m-step stochastic numerical integrators. From observations of trajectory samples, we introduce methods for learning long-time predictors and stable representations of the dynamics. Our approaches use discriminators based on Maximum Mean Discrepancy (MMD), training protocols using both conditional and marginal distributions, and methods for learning dynamic responses over different time-scales. We show how our approaches can be used for modeling physical systems to learn force-laws, damping coefficients, and noise-related parameters. Our adversarial learning approaches provide methods for obtaining stable generative models for dynamic tasks including long-time prediction and developing simulations for stochastic systems.

• Artificial intelligence (AI) / machine learning ↗

Self-testing of a single quantum system from theory to experiment

Abstract Self-testing allows one to characterise quantum systems under minimal assumptions. However, existing schemes rely on quantum nonlocality and cannot be applied to systems that are not entangled. Here, we introduce a robust method that achieves self-testing of individual systems by taking advantage of contextuality. The scheme is based on the simplest contextuality witness for the simplest contextual quantum system—the Klyachko-Can-Binicioğlu-Shumovsky inequality for the qutrit. We establish a lower bound on the fidelity of the state and the measurements as a function of the value of the witness under a pragmatic assumption on the measurements. We apply the method in an experiment on a single trapped 40 Ca + using randomly chosen measurements and perfect detection efficiency. Using the observed statistics, we obtain an experimental demonstration of self-testing of a single quantum system.

Physics↗

Winter School: Applications of Artificial Intelligence to Topics in Nuclear Physics

The virtual topical Winter School on Artificial Intelligence in Nuclear Physics aimed at giving the participants a deeper understanding on what Artificial Intelligence and Machine Learning are and how they can be used to analyze data, design new detectors, controls, and calibration systems for nuclear physics experiments and perform theoretical calculations of nuclear many-body systems.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

The future of self-driving laboratories: from human in the loop interactive AI to gamification

Recent developments in artificial intelligence (AI) and machine learning (ML), implemented through self-driving laboratories (SDLs), are rapidly creating unprecedented opportunities for the accelerated discovery and optimization of materials. This paper provides a joint analysis of SDLs from both academic and industry perspectives, highlighting the importance of integrating human intelligence in these systems. It discusses the necessity of careful planning in SDL design across physical, data, and workflow dimensions, including instrumental setup, experimental workflow, data management, and human–SDL interaction. The significance of integrating human input within SDLs, especially as the focus shifts from individual tools and tasks to the creation and management of complex workflows, is emphasized. The paper stresses on the crucial role of reward function design in developing forward-looking workflows and examines the interplay between hardware evolution, ML application across chemical processes, and the influence of reward systems in research. Ultimately, the article advocates for a future where SDLs blend human intuition in hypothesis formulation with AI's precision, speed, and data-handling capabilities.

97 MATHEMATICS AND COMPUTING↗

Design-to-Deployment Continuum Platform for Microscopes and Computing Ecosystems

Science ecosystems with networked computing systems and physical instruments are increasingly being deployed with a goal to achieve the productivity promised by AI-supported remote automation. In support of these efforts, the virtual infrastructure twins (VITs) have been successfully utilized to develop the orchestration codes for these ecosystems without requiring physical access to expensive instruments, such as electron microscopes. Currently, the utility of such a VIT is severely limited by the computing capacity and capability of the computing system used as its host. Furthermore, codes developed on the VIT typically need to be transferred and refactored for production use, particularly, on high-performance systems with accelerators. In response, we develop a design-to-deployment continuum platform wherein a VIT runs natively on the ecosystem's own computing system, and thereby facilitates the continual in-situ testing and transition of codes for production use. Here, we describe the development and testing of software for remote microscope steering and GPU-based image reconstruction using this platform on a multi-GPU computing system networked to Nion microscopes. We demonstrate a continual transition of steering and reconstruction codes developed under VIT platform to production ecosystem deployment.

Al-Najjar, Anees [Oak Ridge National Laboratory (O↗

Building an AI-enhanced modeling framework to address multiscale predictability challenges

Focal Area(s): Build an AI-enhanced modeling framework that integrates the three focus areas in the solicitation. Science Challenge: The 4M-2N complexities (Multiscale, Multiphysics, Multibody, Multidimension, Non-linearity, and Non-Gaussianality) of atmospheric aerosol-cloud-precipitation-turbulence-radiation system poses physical and computational challenges to further advance predictive models; We plan to address the challenges by developing an AI-enhanced modeling framework that facilitates automated calibration and improvement of subgrid parameterizations, enhance data assimilation of measurements to improve initial and boundary conditions used to drive the physical model, and optimally blends data-driven and physics-based forecasting models.

54 ENVIRONMENTAL SCIENCES↗

Brady Geodatabase for Geothermal Exploration Artificial Intelligence

These files contain the geodatabases related to Brady's Geothermal Field. It includes all input and output files for the Geothermal Exploration Artificial Intelligence. Input and output files are sorted into three categories: raw data, pre-processed data, and analysis (post-processed data). In each of these categories there are six additional types of raster catalogs which are titled Radar, SWIR, Thermal, Geophysics, Geology, and Wells. These inputs and outputs were used with the Geothermal Exploration Artificial Intelligence to identify indicators of blind geothermal systems at the Brady Hot Springs Geothermal Site. The included zip file is a geodatabase to be used with ArcGIS and the tar file is an inclusive database that encompasses the inputs and outputs for the Brady Hot Springs Geothermal Site.

15 GEOTHERMAL ENERGY↗

Navigating the Noise: Bringing Clarity to ML Parameterization Design With O $\boldsymbol{\mathcal{O}}$(100) Ensembles

Abstract Machine‐learning (ML) parameterizations of subgrid processes (here of turbulence, convection, and radiation) may one day replace conventional parameterizations by emulating high‐resolution physics without the cost of explicit simulation. However, uncertainty about the relationship between offline and online performance (i.e., when integrated with a large‐scale general circulation model) hinders their development. Much of this uncertainty stems from limited sampling of the noisy, emergent effects of upstream ML design decisions on downstream online hybrid simulation. Our work rectifies the sampling issue via the construction of a semi‐automated, end‐to‐end pipeline for size ensembles of hybrid simulations, revealing important nuances in how systematic reductions in offline error manifest in changes to online error and online stability. For example, removing dropout and switching from a Mean Squared Error to a Mean Absolute Error loss both reduce offline error, but they have opposite effects on online error and online stability. Other design decisions, like incorporating memory, converting moisture input from specific humidity to relative humidity, using batch normalization, and training on multiple climates do not come with any such compromises. Finally, we show that ensemble sizes of may be necessary to reliably detect causally relevant differences online. By enabling rapid online experimentation at scale, we can empirically settle debates regarding subgrid ML parameterization design that would have otherwise remained unresolved in the noise.

Lin, Jerry [Department of Earth System Sciences Un↗

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging

Fluorescence lifetime imaging (FLI) has been receiving increased attention in recent years as a powerful diagnostic technique in biological and medical research. However, existing FLI systems often suffer from a tradeoff between processing speed, accuracy, and robustness. Inspired by the concept of Edge Artificial Intelligence (Edge AI), we propose a robust approach that enables fast FLI with no degradation of accuracy. This approach couples a recurrent neural network (RNN), which is trained to estimate the fluorescence lifetime directly from raw timestamps without building histograms, to SPAD TCSPC systems, thereby drastically reducing transfer data volumes and hardware resource utilization, and enabling real-time FLI acquisition. We train two variants of the RNN on a synthetic dataset and compare the results to those obtained using center-of-mass method (CMM) and least squares fitting (LS fitting). Results demonstrate that two RNN variants, gated recurrent unit (GRU) and long short-term memory (LSTM), are comparable to CMM and LS fitting in terms of accuracy, while outperforming them in the presence of background noise by a large margin. To explore the ultimate limits of the approach, we derive the Cramer-Rao lower bound of the measurement, showing that RNN yields lifetime estimations with near-optimal precision. To demonstrate real-time operation, we build a FLI microscope based on an existing SPAD TCSPC system comprising a 32 x 32 SPAD sensor named Piccolo. Four quantized GRU cores, capable of processing up to 4 million photons per second, are deployed on the Xilinx Kintex-7 FPGA that controls the Piccolo. Powered by the GRU, the FLI setup can retrieve real-time fluorescence lifetime images at up to 10 frames per second. The proposed FLI system is promising and ideally suited for biomedical applications, including biological imaging, biomedical diagnostics, and fluorescence-assisted surgery, etc.

47 OTHER INSTRUMENTATION↗

The Future of a Myriad of Accelerated Biodiscoveries Lies in AI‐Powered Mass Spectrometry and Multiomics Integration

The intersection of modern artificial intelligence (AI) and mass spectrometry (MS) is set to transform the MS‐based “omics” research fields, particularly proteomics, metabolomics, lipidomics, and glycomics, enabling advancements across a wide range of domains, from health to environment and industrial biotechnology. Beginning with an overview of key challenges inherent in MS software pipelines, this personal perspective explores how AI‐driven solutions can address them to enhance data processing, integration and interpretation. It proposes a paradigm shift in molecular identification and quantitation algorithms, leveraging AI to enable holistic interpretation of MS‐based multiomics data. While centered on MS‐based omics, this holistic AI‐driven paradigm is also critical for connecting dynamic biochemical changes to genomics and transcriptomics contexts, reinforcing the integrative value of MS in multiomics research. Ultimately, this AI‐driven approach could enhance efficiency, accuracy, and molecular breadth of coverage, deepening our systems‐level understanding of biological processes and accelerating a myriad of biodiscoveries.

47 OTHER INSTRUMENTATION↗

Enabling HPC Scientific Workflows for Serverless

The convergence of edge computing, big data analytics, and AI with traditional scientific calculations is increasingly being adopted in HPC workflows. Workflow management systems are crucial for managing and orchestrating these complex computational tasks. However, it is difficult to identify patterns within the growing population of HPC workflows. Serverless has emerged as a novel computing paradigm, offering dynamic resource allocation, quick response time, fine-grained resource management and auto-scaling. In this paper, we propose a framework to enable HPC scientific workflows on serverless. Our approach integrates a widely used traditional HPC workflow generator with an HPC serverless workflow management system to create benchmark suites of scientific workflows with diverse characteristics. These workflows can be executed on different serverless platforms. We comprehensively compare executing workflows on traditional local containers and serverless computing platforms. Our results show that serverless can reduce CPU and memory usage respectively by 78.11% and 73.92% without compromising performance.

Andrei da silva, Anderson↗

Paraview-MCP

This project provides a streamlined way for users to interact with and control powerful scientific visualization software (ParaView) through a conversational interface. By developing an automated "Model Context Protocol" (MCP) server with a Python-based ParaView manager, the system allows users to seamlessly load and visualize complex datasets, explore visualization options with AI assistance, and optimize visualization output in a close loop. This is achieved by issuing intuitive, natural-language commands. The result is a user-friendly interface that integrates high-level conversation and scriptable data visualization, making scientific visualization tools more accessible to a broad audience.

Liu, Shusen [Lawrence Livermore National Laborator↗

AI-Based Upgrades to Observational Data Centers to Facilitate Data Interoperability

Focal Areas: (1) Data acquisition and assimilation enabled by machine learning, AI, and advanced methods including experimental/network design/optimization, unsupervised learning (including deep learning), and hardware-related efforts involving AI (e.g., edge computing). Focal areas 2 and 3 have critical dependencies to the modernization described. Key benefits to the focal areas: (1) Modernized observatory framework capable of agile adaptive observation, (2) Advanced instrument and data tagging supporting AI data acquisition for assimilation or validation, and (3) Widespread data interoperability bridging Earth system prediction scales

54 ENVIRONMENTAL SCIENCES↗

Can the United States Maintain Its Leadership in High-Performance Computing? - A report from the ASCAC Subcommittee on American Competitiveness and Innovation to the ASCR Office

The United States (U.S.) is no longer the unambiguous leader in the vitally important field of high performance computing (HPC). Japan, the European Union (EU), and China have fielded systems that are on par with our fastest supercomputers. The supply chain for everything from semiconductors to scientific software is globally distributed. Yet our economic future and security depend critically on our ability to innovate faster than our competitors, and the speed of innovation depends increasingly on large-scale computational science and engineering and thus HPC. How should the United States respond to this challenge? This report seeks to initiate a new and potentially transformative national discussion on this vital question. The Department of Energy’s (DOE) Advanced Scientific Computing Research (ASCR) program is well-positioned to make informed, targeted decisions about where the United States should cooperate and where it should compete in the global market for scientific exploration and discovery. By setting its sights on problems critical to our nation and the world, by establishing productive new collaborations, and by making strategic investments, ASCR can restore and maintain U.S. scientific leadership in the critical areas described in this report while strengthening our research infrastructure and training a large, diverse cohort of scientists. In doing so, ASCR and its scientists will pave the way for a secure and prosperous future for America. For more than 30 years, the ASCR program has provided the HPC and networking capabilities and expertise needed to support DOE’s mission to advance the national, economic, and energy security of the United States. The program now faces the challenge of developing and deploying the next generation of HPC systems and technologies, as well as supporting the application of HPC and artificial intelligence (AI) technologies to a wide range of scientific and engineering research problems. Through its research and development efforts, the ASCR program must also advance the state of the art in HPC and accelerate the pace of scientific discovery and technological innovation. Fulfilling this promise will require significantly increased investments, as well as innovative policies and programs. This subcommittee is aware that we are making recommendations and calls for action at a time when federal resources are limited. We understand that a wide range of competing priorities must be balanced by the nation’s leaders and that there is a need to leverage resources in new ways and seek efficiencies in facilities and operations. However, we must not let these realities limit our imagination or silence our advocacy. The ASCR program is a key part of the U.S. research infrastructure and an important component of economic growth and U.S. competitiveness. ASCR has a responsibility to pursue its mission, including advanced scientific computing, applications of AI technologies, and the required advanced research facilities, with determination and enthusiasm. To fulfill the scientific enterprise’s responsibility to the nation, the ASCR program must not only develop and publish a clear vision with an associated list of goals, priorities, and recommendations but also demonstrate scientific leadership by consistently securing long-term funding. This will allow the program to build on its achievements to date, to realize its ambitious vision, and to make lasting contributions to the field.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗