An Introduction to the E3SM Special Collection: Goals, Science Drivers, Development, and Analysis
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54 ENVIRONMENTAL SCIENCES↗
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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Visualization and Simulation of Carbon Structures with Higher Genus
The Computational Science Initiative (CSI) department at Brookhaven National Laboratory involves varied and interdisciplinary work through out all sciences. In particular, we worked within the realm of computational chemistry through the means of studying carbon nanotori structures via their visualization and simulation. Compounds like graphene and other carbon allotropes,such as the nanotorus, have been in the eye of scientists for several years due to their unique polyhex structures which provide unique topologies and therefore possibly unique material properties. C++ code to generate Cartesian coordinates for an arbitrary torus was utilized to allow the use of molecular editing and modeling programs, such as Avogadro or Visual Molecular Dynamics (VMD) to design multiple genus nanocarbon toroidal structures. Several files were generated that contained multiple genus to them, namely a double torus, triple torus, and quadruple torus, all based off of a single genus carbon nanotorus from previous said code. Bond angle information was then utilized through .report files generated from structures files (.xyz or .cml) to allow for vertices and thereby neighboring atoms to the vertices to be identified. With these identified atoms for the above multiple genus structures, as well as for the C240 buckyball fullerene and carbon nanotorus, we wrote C++ code to generate an adjacency matrix for each respective one. These matrices were then subsequently used to generate eigenvalues and eigenvectors through a Mathmatica function for each structure. Using these values we can study the energies of these systems and thereby its functionalities and properties.
97 MATHEMATICS AND COMPUTING↗
Brookhaven Lab Hosts Hackathon Focused on Graphics Processing Units (GPUs)
For five days in June 2017, Brookhaven Lab’s Computational Science Initiative hosted “Brookathon”—a programming workshop where teams of researchers, students, and software developers worked with mentors to accelerate scientific applications on GPUs for high-performance computing.
97 MATHEMATICS AND COMPUTING↗
Accelerated Computing Hackathon Returns for Second Year
COMPUTATIONAL SCIENCE INITIATIVE; GPU PROGRAMMING TECHNIQUES; CODING WORKSHOP; CODE; CODING; HACKATHON;
97 MATHEMATICS AND COMPUTING↗
Finalizing Transition to the New Data Center at BNL
Computational science, data management and analysis have been key factors in the success of Brookhaven National Laboratory's scientific programs at the Relativistic Heavy Ion Collider (RHIC), the National Synchrotron Light Source (NSLS-II), the Center for Functional Nanomaterials (CFN), and in biological, atmospheric, and energy systems science, Lattice Quantum Chromodynamics (LQCD) and Materials Science, as well as our participation in international research collaborations, such as the ATLAS Experiment at Europe's Large Hadron Collider (LHC) at CERN (Switzerland) and the Belle II Experiment at KEK (Japan). The construction of a new data center is an acknowledgement of the increasing demand for computing and storage services at BNL in the near term and enable the Lab to address the needs of the future experiments at the High-Luminosity LHC at CERN and the Electron-Ion Collider (EIC) at BNL in the long term. The Computing Facility Revitalization (CFR) project is aimed at repurposing the former National Synchrotron Light Source (NSLS-I) building as the new data center for BNL. The construction of the new data center was finished in 2021Q3, and it was delivered for production in early FY2022 for all collaborations supported by the Scientific Data and Computing Center (SDCC), including STAR, PHENIX and sPHENIX experiments at RHIC collider at BNL, the Belle II Experiment at KEK (Japan), and the Computational Science Initiative at BNL (CSI). This paper highlights the key mechanical, electrical, and networking components of the new data center in its final configuration as used in production since 2021Q4 and gives an overview for the extension of the central network systems into the new data center and the migration of a significant portion of IT load and services from the old data center to the new data center carried out in 20212023, with expected completion of the main phase of the gradual IT equipment replacement and migration from the old data center into the new one set to the end of FY2023 (Sep 30, 2023).
99 GENERAL AND MISCELLANEOUS↗
How Data Becomes Physics: Inside the RACF
The RHIC & ATLAS Computing Facility (RACF) at the U.S. Department of Energy’s (DOE) Brookhaven National Laboratory sits at the center of a global computing network. It connects more than 2,500 researchers around the world with the data generated by millions of particle collisions taking place each second at Brookhaven Lab's Relativistic Heavy Ion Collider (RHIC, a DOE Office of Science User Facility for nuclear physics research), and the ATLAS experiment at the Large Hadron Collider in Europe. Watch this video to learn how the people and computing resources of the RACF serve these scientists to turn petabytes of raw data into physics discoveries.
71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
Application of Quantum Machine Learning to High Energy Physics Analysis at LHC Using Quantum Computer Simulators and Quantum Computer Hardware
Machine learning enjoys widespread success in High Energy Physics (HEP) analyses at LHC. However the ambitious HL-LHC program will require much more computing resources in the next two decades. Quantum computing may offer speed-up for HEP physics analyses at HL-LHC, and can be a new computational paradigm for big data analyses in High Energy Physics.We have successfully employed three methods (1) Variational Quantum Classifier (VQC) method, (2) Quantum Support Vector Machine Kernel (QSVM-kernel) method and (3) Quantum Neural Network (QNN) method for two LHC flagship analyses: ttH (Higgs production in association with two top quarks) and H->mumu (Higgs decay to two muons, the second generation fermions). We shall address the progressive improvements in performance from method (1) to method (3).We will present our experiences and results of a study on LHC High Energy Physics data analyses with IBM Quantum Simulator and Quantum Hardware (using IBM Qiskit framework), Google Quantum Simulator (using Google Cirq framework), and Amazon Quantum Simulator (using Amazon Braket cloud service). The work is in the context of a Qubit platform (a gate-model quantum computer). Taking into account the present limitation of hardware access, different quantum machine learning methods are studied on simulators and the results are compared with classical machine learning methods (BDT, classical Support Vector Machine and classical Neural Network). Furthermore, we do apply quantum machine learning on IBM quantum hardware to compare performance between quantum simulator and quantum hardware. The work is performed by an international and interdisciplinary collaboration with the Department of Physics and Department of Computer Sciences of University of Wisconsin, CERN Quantum Technology Initiative, IBM Research Zurich, IBM T.J. Watson Research Center, Fermilab Quantum Institute, BNL Computational Science Initiative, State University of New York at Stony Brook, and Quantum Computing and AI Research of Amazon Web Services. This work pioneers a close collaboration of academic institutions with industrial corporations in the High Energy Physics analyses effort. Though the size of event samples in future HL-LHC physics and the limited number of qubits pose some challenges to the Quantum Machine learning studies for High Energy Physics, more advanced quantum computers with larger number of qubits, reduced noise and improved running time (as envisioned by IBM and Google) may outperform classical machine learning in both classification power and in speed.Although the era of efficient quantum computing may still be years away, we have made promising progress and obtained preliminary results in applying quantum machine learning to High Energy Physics. A PROOF OF PRINCIPLE.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE FAIR Surrogate Benchmarks Supporting AI and Simulation Research (SBI Surrogate Benchmark Initiative) (Final Report)
Computational Science is being revolutionized by integrating AI and simulation and, in particular, by deep learning surrogate models that can replace all or part of traditional large‐scale HPC computations. Such surrogates can achieve remarkable performance improvements, as much as several orders of magnitude, and save both compute time and energy. The Surrogate Benchmark Initiative (SBI) project creates a community repository and FAIR (Findable, Accessible, Interoperable, and Reusable) data ecosystem for HPC application surrogate benchmarks. The SBI team comes from Argonne National Laboratory (ANL), Indiana University (IU), Rutgers University, the University of Tennessee, Knoxville (UTK), and the University of Virginia(UVA). SBI repositories include data, code, and all relevant collateral artifacts, that the science and engineering community needs to use and reuse these data sets and surrogates. SBI repositories generate active research from both participants in SBI and the broader AI and domain science communities. This project develops surrogates that use several different neural nets to learn and quickly infer the results of simulations and data systems and capture them as surrogate benchmarks with a rich set of metadata, covering. Data; Model; Metrics specification; Machine specification; Science, Speed, Power Results, We research FAIR metadata for these benchmarks. We develop application surrogate examples as benchmarks across many fields (ANL, UTK, IU, UVA). We also study non Surrogate benchmarks that have many common features and similar issues regarding FAIRness. We work with MLCommons (UVA, UTK), which is a major machine learning benchmarking activity where we get metadata ontologies, software, and benchmarks, benchmarks have datasets, models, and metadata, and they need a technical framework developed by UTK and Rutgers and deployed by UVA. We study features of Surrogates, including performance, training set size, and uncertainty quantification (Rutgers, UVA and IU).
97 MATHEMATICS AND COMPUTING↗
NASA's computer science research program
Following a major assessment of NASA's computing technology needs, a new program of computer science research has been initiated by the Agency. The program includes work in concurrent processing, management of large scale scientific databases, software engineering, reliable computing, and artificial intelligence. The program is driven by applications requirements in computational fluid dynamics, image processing, sensor data management, real-time mission control and autonomous systems. It consists of university research, in-house NASA research, and NASA's Research Institute for Advanced Computer Science (RIACS) and Institute for Computer Applications in Science and Engineering (ICASE). The overall goal is to provide the technical foundation within NASA to exploit advancing computing technology in aerospace applications.
Larsen, R. L.↗
Final report- UFL - RAPIDS2: A SciDAC Institute for Computer Science, Data, and Artificial Intelligence
The research initiatives supported by the U.S. Department of Energy (DOE) Grant DE-SC0022265 are fundamentally aimed at pioneering advanced machine learning (ML) techniques for scientific data compression within high-performance computing (HPC) environments. This comprehensive body of work addresses the critical challenge posed by the exponential growth of data generated by scientific simulations in domains such as fusion energy, climate modeling, and computational fluid dynamics (CFD). A core objective is to develop compression algorithms that achieve substantial data reduction—often by orders of magnitude—while rigorously ensuring the fidelity of both the primary data (PD) and scientifically crucial derived quantities of interest (QoI). The methodologies deployed under this grant integrate sophisticated deep learning architectures, prominently featuring autoencoders, advanced generative models like conditional diffusion, and hybrid learning techniques. Key innovations include the development of Guaranteed Autoencoders (GAE) and the Guaranteed Conditional Diffusion with Tensor Correction (GCDTC) framework, which provide explicit, instance-level error bounds on reconstructed data. Furthermore, specialized strategies such as nonlinear constraint satisfaction are employed to preserve the integrity of QoI, a vital requirement for the trustworthiness of downstream scientific analyses. This research also focuses on the design and implementation of scalable, GPU-accelerated software pipelines that seamlessly integrate into existing HPC workflows, ensuring both computational efficiency and practical applicability. The CAESAR framework, for example, unifies foundation and generative models to create an adaptive and efficient compression solution for spatio-temporal scientific data. Collectively, these efforts represent a significant advancement in mitigating the scientific data deluge, enabling more effective data management, accelerated scientific discovery, and optimized utilization of HPC resources.
97 MATHEMATICS AND COMPUTING↗
Automated metadata--final project report
This report summarizes the work of the Automated Metadata, Provenance Cataloging, and Navigable Interfaces: Ensuring the Usefulness of Extreme-Scale Data Project (MPO Project) funded by the United States Department of Energy (DOE), Offices of Advanced Scientific Computing Research and Fusion Energy Sciences. Initially funded for three years starting in 2012, it was extended for 6 months with additional funding. The project was a collaboration between scientists at General Atomics, Lawrence Berkley National Laboratory (LBNL), and Massachusetts Institute of Technology (MIT). The group leveraged existing computer science technology where possible, and extended or created new capabilities where required. The MPO project was able to successfully create a suite of software tools that can be used by a scientific community to automatically document their scientific workflows. These tools were integrated into workflows for fusion energy and climate research illustrating the general applicability of the project’s toolkit. Feedback was very positive on the project’s toolkit and the value of such automatic workflow documentation to the scientific endeavor.
96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA's Participation in the National Computational Grid
Over the last several years it has become evident that the character of NASA's supercomputing needs has changed. One of the major missions of the agency is to support the design and manufacture of aero- and space-vehicles with technologies that will significantly reduce their cost. It is becoming clear that improvements in the process of aerospace design and manufacturing will require a high performance information infrastructure that allows geographically dispersed teams to draw upon resources that are broader than traditional supercomputing. A computational grid draws together our information resources into one system. We can foresee the time when a Grid will allow engineers and scientists to use the tools of supercomputers, databases and on line experimental devices in a virtual environment to collaborate with distant colleagues. The concept of a computational grid has been spoken of for many years, but several events in recent times are conspiring to allow us to actually build one. In late 1997 the National Science Foundation initiated the Partnerships for Advanced Computational Infrastructure (PACI) which is built around the idea of distributed high performance computing. The Alliance lead, by the National Computational Science Alliance (NCSA), and the National Partnership for Advanced Computational Infrastructure (NPACI), lead by the San Diego Supercomputing Center, have been instrumental in drawing together the "Grid Community" to identify the technology bottlenecks and propose a research agenda to address them. During the same period NASA has begun to reformulate parts of two major high performance computing research programs to concentrate on distributed high performance computing and has banded together with the PACI centers to address the research agenda in common.
Feiereisen, William J.↗
Kennedy Space Center ITC-1 Internship Overview
As an intern for Priscilla Elfrey in the ITC-1 department, I was involved in many activities that have helped me to develop many new skills. I supported four different projects during my internship, which included the Center for Life Cycle Design (CfLCD), SISO Space Interoperability Smackdown, RTI Teacher Mentor Program, and the Discrete Event Simulation Integrated Visualization Environment Team (DIVE). I provided the CfLCD with web based research on cyber security initiatives involving simulation, education for young children, cloud computing, Otronicon, and Science, Technology, Engineering, and Mathematics (STEM) education initiatives. I also attended STEM meetings regarding simulation courses, and educational course enhancements. To further improve the SISO Simulation event, I provided observation feedback to the technical advisory board. I also helped to set up a chat federation for HLA. The third project involved the RTI Teacher Mentor program, which I helped to organize. Last, but not least, I worked with the DIVE team to develop new software to help visualize discrete event simulations. All of these projects have provided experience on an interdisciplinary level ranging from speech and communication to solving complex problems using math and science.
Ni, Marcus↗
A survey of software implementations used by application codes in the Exascale Computing Project
The US Department of Energy Office of Science and the National Nuclear Security Administration initiated the Exascale Computing Project (ECP) in 2016 to prepare mission-relevant applications and scientific software for the delivery of the exascale computers starting in 2023. The ECP currently supports 24 efforts directed at specific applications and six supporting co-design projects. These 24 application projects contain 62 application codes that are implemented in three high-level languages—C, C++, and Fortran—and use 22 combinations of graphical processing unit programming models. The most common implementation language is C++, which is used in 53 different application codes. The most common programming models across ECP applications are CUDA and Kokkos, which are employed in 15 and 14 applications, respectively. This article provides a survey of the programming languages and models used in the ECP applications codebase that will be used to achieve performance on the future exascale hardware platforms.
97 MATHEMATICS AND COMPUTING↗
Carbon diffusion in molten uranium: an ab initio molecular dynamics study
In this work we used ab initio molecular dynamics (AIMD) within the framework of density functional theory (DFT) and the projector-augmented wave (PAW) method to study carbon diffusion in liquid uranium at temperatures above 1600 K. The electronic interactions of carbon and uranium were described using the local density approximation (LDA). The self-diffusion of uranium based on this approach is compared with literature computational and experimental results for liquid uranium. The temperature dependence of carbon and uranium diffusion in the melt was evaluated by fitting the resulting diffusion coefficients to an Arrhenius relationship. We found that the LDA calculated activation energy for carbon was nearly twice that of uranium: 0.55±0.03 eV for carbon compared to 0.32±0.04 eV for uranium. Structural analysis of the liquid uranium-carbon system is also discussed.
Molecular dynamics computer simulation, Nuclear Pr↗
The NASA Carbon Monitoring System Phase 2 synthesis: scope, findings, gaps and recommended next steps
Abstract Underlying policy efforts to address global climate change is the scientific need to develop the methods to accurately measure and model carbon stocks and fluxes across the wide range of spatial and temporal scales in the Earth system. Initiated in 2010, the NASA Carbon Monitoring System is one of the most ambitious relevant science initiatives to date, exploiting the satellite remote sensing resources, computational capabilities, scientific knowledge, airborne science capabilities, and end-to-end system expertise that are major strengths of the NASA Earth Science program. Here we provide a synthesis of ‘Phase 2’ activities (2011–2019), encompassing 79 projects, 482 publications, and 136 data products. Our synthesis addresses four key questions: What has been attempted? What major results have been obtained? What major gaps and uncertainties remain? and What are the recommended next steps? Through this review, we take stock of what has been accomplished and identify future priorities toward meeting the nation’s needs for carbon monitoring reporting and verification.
54 ENVIRONMENTAL SCIENCES↗
Scientific Computation Application Partnerships in Materials and Chemical Sciences, Charge Transfer and Charge Transport in Photoactivated Systems, Developing Electron-Correlated Methods for Excited State Structure and Dynamics in the NWChem Software Suite
Charge transfer and charge transport in photoactivated systems are fundamental processes that underlie solar energy capture, solar energy conversion, and photoactivated catalysis, both organometallic and enzymatic. We developed methods, algorithms, and software tools needed for reliable treatment of the underlying physics for charge transfer and charge transport, an undertaking with broad applicability to the goals of the fundamental-interaction component of the Department of Energy Office of Basic Energy Sciences and the exascale initiative of the Office of Advanced Scientific Computing Research.
14 SOLAR ENERGY↗
Examples of Mission-driven Data Science from Jefferson Lab and ACES
This presentation details mission-driven data science initiatives at Jefferson Lab and the Joint Institute for Advanced Computing on Environmental Studies (ACES). JLab, a U.S. Department of Energy Office of Science national laboratory, operates the Continuous Electron Beam Accelerator Facility (CEBAF), and is the lead institute for the new High Performance Data Facility (HPDF) Hub. The Joint Institute for ACES brings together interdisciplinary teams in health informatics, climate modeling, computer science, and physics to address environmental challenges, including flood modeling. The Hampton Roads region, particularly Norfolk and Virginia Beach, faces increasing flood risks, motivating the need for rapid, reliable, and risk-aware decision support. ACES’s flooding work has a focus on uncertainty quantification (UQ) and machine learning (ML) for coastal flood management. The work is motivated by the increasing vulnerability of communities such as Norfolk and Virginia Beach, Virginia, to frequent coastal flooding events, and the need for rapid, reliable decision support. The research develops computationally efficient ML surrogate models to forecast water levels and flooding risk. A central theme is the quantification and calibration of predictive uncertainty, especially for out-of-distribution (OOD) scenarios, using techniques such as Monte Carlo Dropout, Deep Ensembles, Gaussian Processes, and Deep Quantile Regression (DQR). The study demonstrates that distance-aware UQ is critical for reliable scientific AI, particularly in high-dimensional, safety-critical, and real-time applications.
McSpadden, Diana [Thomas Jefferson National Accele↗