This project, aiming to address fundamental questions in elementary particle physics, consists of three related themes: (1) the hadronic vacuum polarization contribution to the anomalous magnetic moment of the muon; (2) semileptonic decays of B and D mesons; and (3) CP violation.

This work will facilitate and significantly speed up the quantitative description of crucial gas- phase and coupled heterogeneous catalyst/gas-phase chemical systems. Such tools promise to enable revolutionary advances in predictive catalysis, crucial to addressing DOE grand challenges, including both energy storage and chemical transformations.

To speed up the procedures involved with drug discovery, this team is using state-of-the-art supercomputers to make personalized predictions about treatment outcomes.

This project advances scalable manufacturing of quantum materials and ultrafast control of their emergent properties on demand using AI-guided exascale quantum dynamics simulations in tandem with state-of-the-art x-ray, electron-beam and neutron experiments at DOE facilities.

Using high performance computations, this project will determine the physics that controls giant molecules (polymers) that consist of several distinct chemical blocks, and the process by which these molecules can be transformed into viable materials for new uses including clean energy and biomedical technologies.

By running massive simulations of magnetized turbulent astrophysical plasma t, this project will determine the long-debated source of cosmic ray scattering, which limits understanding of galaxy formation and black hole growth. The simulations will provide the ideal environment for cosmic ray propagation and unveil the underlying nature of turbulence.

This project aims to research Mellin moments of the proton generalized parton distributions. Numerical simulations will be performed with quark masses as encountered in nature.

A snapshot figure of turbulence driven, space-time fluctuating homoclinic tangle near the magnetic X-point of ITER edge, found for the first time from XGC's INCITE simulation. This space-time fluctuating homoclinic tangle could be the hidden mechanism to connect the plasmas between the burning core and the divertor plasmas, which the fusion researchers have been searching for. Simulation by S. Ku (PPPL).  Visualization by D. Pugmire and J. Choi (ORNL)."

The overarching goal of this INCITE project is to create, analyze, publish, and curate a large suite of state-of-the-art long-term 3D core-collapse supernova explosion simulations that will constitute the standard 3D model of core-collapse supernova explosions for years to come. 

This project will use artificial intelligence to build tools that predict interactions between any two proteins and will make these tools widely available to the biology community.

This project will develop fundamental theory of heterogeneous thermal and electrocatalysis, and a realistic statistical and dynamical description of the catalytic interface in reaction conditions. This will enable the understanding of catalytic mechanisms, and the design of new efficient catalysts.

This project focuses on using LES to reproduce the flow behavior and collect the turbulent statistics in an involute coolant channel for high Reynolds number and using Large Eddy Simulation to provide a benchmark for the highly simplified involute coolant channel.

These simulations carried out by this team will help design new materials that can withstand the harsh environments created by fusion reactors.

This team will everage data generated with high-fidelity CFD simulations to inform fast-running model development for design tools, aiding in the deployment of carbon-free energy on a commercial scale.

This project will use leadership class computing resources to run comparative probabilistic sensitivity analyses (PSAs) of screening strategies with three state-of-the-art CRC models. 

This ALCC project will perform machine learning molecular dynamics simulations at DOE exascale Frontier and Aurora supercomputers at experimental time and length scales to uncover complex response of a-C ablators under dynamic compression and guide experiments to observe predicted phenomena and validate our theoretical models. 

This project builds upon the strategic partnership between the US Department of Veterans Affairs (VA) and DOE in leveraging DOE computing capabilities to enhance the health outcomes of veterans through the MVP-CHAMPION (Million Veteran Program Computational Health Analytics for Medical Precision to Improve Outcomes Now) project.

This research team, along with other members of the RBC & UKQCD collaborations, performed the first complete lattice calculation of the decay in 2015, and in 2020 published an improved result with significantly better control over the systematic errors.

This research is aimed at using large-scale, high-performance computing to assist discovery of novel batery electrolytes to enable rational design of superior electrolytes for high voltage batteries.

This team set out to use GPU resources at ALCF including Polaris and Aurora to identify the fundamental constituents of matter, to precisely measure the forces between them, to identify new symmetries, and to search for dark matter.