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Idaho National Laboratory Fuel Cycle Science & Technology Overview Wyoming State Legislature Joint Committee on Minerals, Business & Economic Development Visit April 18, 2023
A PowerPoint on Enhancing High Performance Computing (HPC) User Support with an AI Assistant through a Science Gateway.
PowerPoint slides on Nuclear Science User Facilities (NSUF) Overview
The next-generation Particle X-ray Temporal Diagnostic (PXTD) has been implemented for simultaneous measurements of x-ray, charged particle, and neutron emission histories from a wide range of inertial confinement fusion and high energy density plasma experiments, demonstrating excellent timing accuracy and greatly improved experimental flexibility. The key changes to the previously fielded system are a redesigned set of thin foil filters in front of the scintillators and individual neutral density filters for each region of the detector. The fully implemented PXTD system can provide unique information about the evolution of ion and electron temperatures in multi-ion and kinetic-physics experiments, proton radiography experiments, and DT experiments executed at the OMEGA facility. The system has 35 ps time resolution and negligible relative timing uncertainty between measured emission history signals. The first use of the upgraded four-channel PXTD system during a set of D 3 He-filled silica-glass implosions on OMEGA captured electron temperatures with a minimum uncertainty of 0.5 keV and resolved both the relative timings and widths of neutron, proton, and x-ray peaks within a single recorded image.
Here this Special Issue of Propellants, Explosives, Pyrotechnics (PEP) is focused on energetic materials discovery and design using Data Science and Machine Learning (DS&ML). The application of DS&ML has proven to be transformative in many areas, where it has been shown to expedite analysis, enable extraction of greater quantities of information from datasets, and guide experiments. However, energetic materials and their applications present unique challenges that often hinder the use of standardized tools and practices. In spite of these challenges, important and compelling advancements are being made toward data-directed research in energetics.
Abstract One among several advantages of measure transport methods is that they allow or a unified framework for processing and analysis of data distributed according to a wide class of probability measures. Within this context, we present results from computational studies aimed at assessing the potential of measure transport techniques, specifically, the use of triangular transport maps, as part of a workflow intended to support research in the biological sciences. Scenarios characterized by the availability of limited amount of sample data, which are common in domains such as radiation biology, are of particular interest. We find that when estimating a distribution density function given limited amount of sample data, adaptive transport maps are advantageous. In particular, statistics gathered from computing series of adaptive transport maps, trained on a series of randomly chosen subsets of the set of available data samples, leads to uncovering information hidden in the data. As a result, in the radiation biology application considered here, this approach provides a tool for generating hypotheses about gene relationships and their dynamics under radiation exposure.
Photovoltaics reliability for terrestrial applications has more than four decades of history and experience. International standards development has prevented many early life failures and must be continued in the future. Yet, as the module product development cycle is measured in mere months, additional investment in the fundamental degradation science and rapid detection should be developed. In this article, a brief strategy is provided addressing indoor accelerated, outdoor testing, and modeling efforts required to retain high reliability in the following decade.
The current focus on artificial intelligence and machine learning in the scientific community has the potential to greatly speed up discovery. In this article, we explore what a “smart facility” would mean for materials science. We propose to capture meta-data at every step of an experiment, including materials synthesis, sample production and characterization, simulation, and the analysis software used to extract information. Although most of this information is captured in various institutional systems and staff logbooks, more insight could be obtained by connecting this information through a system that allows automation. AI-enabled processes built on such a system would have the potential of making experiment planning easier and minimize the time between experiment and publication.
The Near-Earth Space Radiation and Plasma Environment falls within the realm of G3 Cluster (G3 refers to ‘Near-Earth Radiation and Plasma Environment’ of the ‘Coupled Geospace System’) under the COSPAR (Committee On Space Research) /International Space Weather Action Teams (ISWAT) Initiative. The diverse and dynamic particle populations from this region pose challenges from both science and space weather-impact perspectives. The G3 cluster has intimate connections with solar, heliosphere clusters, and the other Geospace ones (G1, G2) through a chain of physical processes. This paper reviews recent scientific advances in understanding this complex space environment, identifies gaps in research and space weather applications, and maps out our recommendations on priorities for the next 5-10 years.
In this work, a thermo-mechanics model that relies on creating the material property definition framework (MPDF) and multiphysics coupling of the heat transfer and the solid mechanics modules is developed to determine the structural integrity of the recently designed dual cooled lead lithium (DCLL) inboard blanket (IB) for the Fusion Nuclear Science Facility under steady state loads. The MPDF is called to supply fusion relevant neutron irradiation and temperature induced changes in material properties during multiphysics finite element runs, and PbLi temperature profiles are used to approximate Magnetohydrodynamics effect and the nuclear volumetric heating on the PbLi. Neutron irradiation and temperature induced reduction of the yield and ultimate strengths of F82H steel at the first wall (FW) are quantified for one year. A blanket in an assembly with gaps between blanket sectors and another blanket in an assembly with no gaps between blanket sectors, both exposed to radiation damage that lasted for one year are analyzed. Analysis using the elastic ITER structural design criteria for in-vessel components (ITER SDC-IC) design rules and a linear isotropic-hardening-type elastoplastic material model are used where most appropriate. The IB blanket with gaps between blanket sectors will withstand the steady state combined thermal and coolant loads for one year operational period but will fail if no gaps are allowed between blanket sectors. It is recommended that a gap of about 7.62 mm should be provided between IB blanket sectors during assembly which would close up during service, stop neutron streaming, reduce stresses and reduce bending of the FW into the scrape-off layer.
The performance of high-precision mass spectrometry of radioactive isotopes can often be hindered by large amounts of contamination, including molecular species, stemming from the production of the radioactive beam. In this paper, we report on the development of Collision-Induced Dissociation (CID) as a means of background reduction for experiments at TRIUMF's Ion Trap for Atomic and Nuclear science (TITAN). This study was conducted to characterize the quality and purity of radioactive ion beams and the reduction of molecular contaminants to allow for mass measurements of radioactive isotopes to be done further from nuclear stability. Furthermore, this is the first demonstration of CID at an ISOL-type radioactive ion beam facility, and it is shown that molecular contamination can be reduced up to an order of magnitude.
The ENDF/B-VIII.1 library is the newest recommended evaluated nuclear data file by the Cross Section Evaluation Working Group (CSEWG) for use in nuclear science and technology applications, and incorporates advances made in the six years since the release of ENDF/B-VIII.0. Among key advances made are that the 239 Pu file was reevaluated by a joint international effort and that updated 16,18 O, 19 F, 28–30 Si, 50–54 Cr, 55 Mn, 54,56,57 Fe, 63,65 Cu, 139 La, 233,235,238 U, and 240,241 Pu neutron nuclear data from the IAEA coordinated INDEN collaboration were adopted. Over 60 neutron dosimetry cross sections were adopted from the IAEA's IRDFF-II library. In addition, the new library includes significant changes for 3 He, 6 Li, 9 Be, 51 V, 88 Sr, 103 Rh, 140,142 Ce, Dy, 181 Ta, Pt, 206–208 Pb, and 234,236 U neutron data, and new nuclear data for the photonuclear, charged-particle and atomic sublibraries. Numerous thermal neutron scattering kernels were reevaluated or provided for the very first time. On the covariance side, work was undertaken to introduce better uncertainty quantification standards and testing for nuclear data covariances. The significant effort to reevaluate important nuclides has reduced bias in the simulations of many integral experiments with particular progress noted for fluorine, copper, and stainless steel containing benchmarks. Data issues hindered the successful deployment of the previous ENDF/B-VIII.0 for commercial nuclear power applications in high burnup situations. These issues were addressed by improving the 238 U and 239,240,241 Pu evaluated data in the resonance region. The new library performance as a function of burnup is similar to the reference ENDF/B-VII.1 library.
Here, this report describes the physics case, the resulting detector requirements, and the evolving detector concepts for the experimental program at the Electron-Ion Collider (EIC). The EIC will be a powerful new high-luminosity facility in the United States with the capability to collide high-energy electron beams with high-energy proton and ion beams, providing access to those regions in the nucleon and nuclei where their structure is dominated by gluons. Moreover, polarized beams in the EIC will give unprecedented access to the spatial and spin structure of the proton, neutron, and light ions. The studies leading to this document were commissioned and organized by the EIC User Group with the objective of advancing the state and detail of the physics program and developing detector concepts that meet the emerging requirements in preparation for the realization of the EIC. The effort aims to provide the basis for further development of concepts for experimental equipment best suited for the science needs, including the importance of two complementary detectors and interaction regions. This report consists of three volumes. Volume I is an executive summary of our findings and developed concepts. In Volume II we describe studies of a wide range of physics measurements and the emerging requirements on detector acceptance and performance. Volume III discusses general-purpose detector concepts and the underlying technologies to meet the physics requirements. These considerations will form the basis for a world-class experimental program that aims to increase our understanding of the fundamental structure of all visible matter.
Can data science guide researchers toward understanding superconductivity or discover new superconductors? We examine this question in light of a study in this issue of Patterns by Liu et al., who find that the superconducting transition temperature and certain computed energy intervals of the valence band are correlated.
This review article describes the co-evolution of structural biology as a discipline and the Protein Data Bank (PDB), established in 1971 as the first open-access data resource in biology by like-minded structural scientists. As the PDB archive grew in size and scope to encompass macromolecular crystallography, NMR spectroscopy, and cryo-electron microscopy, new technologies were developed to ingest, validate, curate, store, and distribute the information. Community engagement ensured that the needs of structural biologists (data depositors) and data consumers were met. Today, the archive houses more than 230,000 experimentally determined structures of proteins, nucleic acids, and macromolecular machines and their complexes with one another and small-molecule ligands. Aggregate costs of PDB data preservation are ~1% of the cost of structure determination. The enormous impact of PDB data on basic and applied research and education across the natural and medical sciences is presented and highlighted with illustrative examples. Enablement of de novo protein structure prediction (AlphaFold2, RoseTTAfold, OpenFold, etc.) is the most widely appreciated benefit of having a corpus of rigorously validated, expertly curated 3D biostructure data.
Self-driving laboratories (SDLs) promise an accelerated application of the scientific method. Through the automation of experimental workflows, along with autonomous experimental planning, SDLs hold the potential to greatly accelerate research in chemistry and materials discovery. This review provides an in-depth analysis of the state-of-the-art in SDL technology, its applications across various scientific disciplines, and the potential implications for research and industry. This review additionally provides an overview of the enabling technologies for SDLs, including their hardware, software, and integration with laboratory infrastructure. Most importantly, this review explores the diverse range of scientific domains where SDLs have made significant contributions, from drug discovery and materials science to genomics and chemistry. We provide a comprehensive review of existing real-world examples of SDLs, their different levels of automation, and the challenges and limitations associated with each domain.
While multiconfigurational approaches have long been relegated to expert practitioners working on a case-by-case basis, recent developments have increasingly made these methods more routine and applicable to broader sets of systems. This article outlines the state-of-the-art in multiconfigurational approaches, with an emphasis on moving from delicate hand-selected pathways through configuration space toward more robust and efficient approaches to treating a host of challenging chemical systems accurately. First, we overview recent work in automated active-space selection, which has enabled increasingly large-scale applications of multireference methods to modeling vertical excitations and reactivity. Second, we highlight the increasingly efficient methods for recovering correlation energy beyond the active space, as headlined by extensions of pair-density functional theory and its role in accurate and efficient treatment of excited-state dynamics and its utilization to train machine-learned potentials. Finally, we highlight recent efforts to treat extended systems that until recently have lied beyond the traditional limits of active-space methods, giving center stage to product-form wave functions of the localized active space family of methods that allow for the computation of multiconfigurational band structures. These recent advancements point to a broader use of multireference approaches for high-impact chemical and materials science applications.
Large language models (LLMs) have shown remarkable potential in various domains but often lack the ability to access and reason over domain-specific knowledge and tools. In this article, we introduce Chemistry Agent Connecting Tool-Usage to Science (CACTUS), an LLM-based agent that integrates existing cheminformatics tools to enable accurate and advanced reasoning and problem-solving in chemistry and molecular discovery. We evaluate the performance of CACTUS using a diverse set of open-source LLMs, including Gemma-7b, Falcon-7b, MPT-7b, Llama3-8b, and Mistral-7b, on a benchmark of thousands of chemistry questions. Our results demonstrate that CACTUS significantly outperforms baseline LLMs, with the Gemma-7b, Mistral-7b, and Llama3-8b models achieving the highest accuracy regardless of the prompting strategy used. Moreover, we explore the impact of domain-specific prompting and hardware configurations on model performance, highlighting the importance of prompt engineering and the potential for deploying smaller models on consumer-grade hardware without a significant loss in accuracy. By combining the cognitive capabilities of open-source LLMs with widely used domain-specific tools provided by RDKit, CACTUS can assist researchers in tasks such as molecular property prediction, similarity searching, and drug-likeness assessment.