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FY 2024 Annual Report including major accomplishments, utilization and user institution statistics, and project summary reports for FY-24 for Idaho National Laboratory High Performance Computing.
FY 2024 Annual Report including major accomplishments, utilization and user institution statistics, and project summary reports for FY-24 for Idaho National Laboratory High Performance Computing.
We present a language-model-driven agentic artificial intelligence (AI) system to autonomously execute multistage physics experiments on a production synchrotron light source. Implemented at the Advanced Light Source particle accelerator, the system translates natural language user prompts into structured execution plans that combine archive data retrieval, control-system channel resolution, automated script generation, controlled machine interaction, and analysis. In a representative machine physics task, we show that preparation time was reduced by 2 orders of magnitude relative to manual scripting even for a system expert, while operator-standard safety constraints were strictly upheld. Core architectural features, plan-first orchestration, bounded tool access, and dynamic capability selection, enable transparent, auditable execution with fully reproducible artifacts. These results establish a blueprint for the safe integration of agentic AI into accelerator experiments and demanding machine physics studies, as well as routine operations, with direct portability across accelerators worldwide and, more broadly, to other large-scale scientific infrastructures.
Schematic representation of the robotic beamline scientist system. The architecture integrates a sample database, ROS2 Action service, and Bluesky tooling to facilitate adaptive robotic sample management.
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Abstract Beamline experiments at central facilities are increasingly demanding of remote, high-throughput, and adaptive operation conditions. To accommodate such needs, new approaches must be developed that enable on-the-fly decision making for data intensive challenges. Reinforcement learning (RL) is a domain of AI that holds the potential to enable autonomous operations in a feedback loop between beamline experiments and trained agents. Here, we outline the advanced data acquisition and control software of the Bluesky suite, and demonstrate its functionality with a canonical RL problem: cartpole. We then extend these methods to efficient use of beamline resources by using RL to develop an optimal measurement strategy for samples with different scattering characteristics. The RL agents converge on the empirically optimal policy when under-constrained with time. When resource limited, the agents outperform a naive or sequential measurement strategy, often by a factor of 100%. We interface these methods directly with the data storage and provenance technologies at the National Synchrotron Light Source II, thus demonstrating the potential for RL to increase the scientific output of beamlines, and layout the framework for how to achieve this impact.
TimePix3 is a 65k hybrid pixel readout chip with simultaneous Time-of-Arrival (ToA) and Time-over-Threshold (ToT) recording in each pixel*. The chip operates without a trigger signal with a sparse readout where only pixels containing events are read out. The flexible architecture allows 40 MHits/s/cm² readout throughput, using simultaneous readout and acquisition by sharing readout logic with transport logic of superpixel matrix formed using 2x4 structure. The chip ToA records 1.5625 ns time resolution. The X-ray and charged particle events are counted directly. However, indirect neutron counts use 6Li fission in a scintillator matrix, such as ZnS(Ag). The fission space-charge region is limited to 5-9 um. A photon from scintillator material excites a photocathode electron, which is further multiplied in dual-stack MCP. The neutron count event is a cluster of electron events at the chip. We report on the EPICS areaDetector** ADTimePix3 driver that controls Serval*** using json commands. The driver directs data to storage and to a real-time processing pipeline and configures the chip. The time-stamped data are stored in raw .tpx3 file format and passed through a socket where the clustering software identifies individual neutron events. The conventional 2D images are available as images for each exposure frame, and a preview is useful for sample alignment. The areaDetector driver allows integration of time-enhanced capabilities of this detector into SNS beamlines controls and unprecedented time resolution.
This report highlights a subset of accomplishments including projects that were completed in FY 2020 by researchers using Idaho National Laboratory's High-Performance Computing (HPC) resources.
Abstract not provided.
On December 1, 2023, the Director of the of the Office of Science (SC) charged all the Department of Energy Office of Science Federal Advisory Committees to respond to look toward the scientific horizon and identify what new or upgraded facilities will best serve the SC community needs in the next ten years (2024-2034). This report by the Basic Energy Sciences Advisory Committee (BESAC) assess facilities for the Basic Energy Sciences (BES) program.
This presentation lays out how the NSTX-U Research Program will help close the science and technology gaps for moving towards a Fusion Pilot Plant (FPP) and how this work is aligned with the Bold Decadal Vision. The science gap focuses on determining whether the good confinement trends in Spherical Tokamaks extends to lower collisionalities that are comparable to those expected in FPPs. The technology gap is one of mitigating the power exhaust in FPPs, and NSTX-U is uniquely positioned to test Liquid Metal components in the highest heat flux environment of any existing tokamak, spherical or otherwise.
A compilation of all necessary and useful information needed to allow the inexperienced user to program on the PDP 11/70. Information regarding the use of editing and file manipulation utilities as well as operational procedures are included. The inexperienced user is taken through the process of creating, editing, compiling, task building and debugging his/her FORTRAN program. Also, documentation on additional software is included.
The EBIT Calorimeter Spectrometer (ECS) has recently been completed and is currently being installed at the EBIT facility at the Lawrence Livermore National Laboratory. The ECS will replace the smaller XRS/EBIT spectrometer that has been in almost continuous operation since 2000. The XRS/EBIT was based on a spare laboratory cryostat and an engineering model detector system from the Suzaku/XRS observatory. The new ECS spectrometer was built from the ground up to be a low maintenance, high performance microcalorimeter spectrometer with 4 eV resolution at 6 keV, 32 detector channels, 10 us event timing, and capable of uninterrupted acquisition sessions of over 70 hours at 50 mK. The XRSIEBIT program has been extremely successful, producing over two-dozen refereed publications on topics such as laboratory astrophysics, atomic physics, nuclear physics, and calibration of the spectrometers for the National Ignition Facility, with many more publications in preparation. The ECS spectrometer will continue this work into the future with improved spectral resolution, integration times, and ease-of-use. We designed the ECS instrument with TES detectors in mind by using the same highly successful magnetic shielding as our laboratory TES cryostats. This design will lead to a future TES instrument at the LLNL EBIT. This proposed future instrument would include a hybrid detector system with 0.8 eV resolution in the band from 0.1-1.0 keV, 2 eV from 0.1-10 keV, and 30 eV from 0.5-100 keV, with high quantum efficiency in each band. Here we discuss the legacy of the XRS/EBIT program, the performance of the new ECS spectrometer, and plans for a future TES spectrometer.
The Glenn Extreme Environment Rig (GEER) is a large capacity pressure vessel capable of simulating high pressure and temperature conditions with a specified, custom mixture of gas. The chamber is certified for operation in physical and chemical conditions representative of the surface conditions of Venus. It can accommodate large scientific or engineering test articles while maintaining the specified conditions indefinitely. The user-configurable gas supply is capable of handling a mixture of up to nine independent streams for injection directly into the vessel. GEER is supported in part by NASA’s Planetary Science Division under the Science Mission Directorate and is available for use by the commercial, academic, and research communities. GEER was operationally commissioned in November of 2014. At that time, it successfully maintained the temperature, pressure, and chemistry of the Venus surface atmosphere. In the spring of 2015, GEER began operations for engineering and scientific research. Since then, over 20 tests have been successfully conducted in the chamber with a variety of atmospheric test conditions.
The project aims to reduce feedstock variability using a quality-by-design approach beginning when the biomass is introduced to the process and will continue through the size reduction process, which will yield the results of fine generation reduction, contaminant removal, control of the physical and chemical critical material attributes in the process, and management of the flowability.
Artificial Intelligence and Machine Learning Software Training for Researchers
In FY21, INL HPC capabilities were utilized by a diverse set of computing and applied researchers, for a wide range of research and engineering activities. This report focuses on current INL HPC systems and utilization from October 1, 2020 through September 30, 2021.
Presentation on NETL DAC facility.