This project seeks study laser plasma interactions on meaningful spatial and temporal scales of relevance to various inertial fusion energy scenarios.

This project uses the gyrokinetic particle-in-cell code XGC to study fundamental edge physics issues critical to the success of ITER and the magnetic fusion energy programs.

This project combines a highly scalable computational fluid dynamics solver with anisotropically adapted unstructured grids to enable flow simulations of unprecedented scale and complexity on Theta, gaining insight into questions of 3D active flow control.

The team will use Theta to carry out simulations aimed at advancing the design of next-generation nuclear reactors. Their project will perform high-fidelity calculations of the flow and heat transfer behavior for pebble bed, gas-cooled reactors and force fluctuation in a fuel assembly with spacer grids.

This project will advance fusion energy research by performing large-scale simulations to shed light on plasma surface interactions. The team will use Theta to study the response of tungsten, the proposed ITER divertor, to low-energy, mixed H-He plasma exposure in the presence of impurity atoms.

Researchers will use Theta to reconstruct the full dataset of neutrinos from the NuMI neutrino beam using the MicroBooNE detector at Fermilab, and to perform high-precision measurements of the electron-neutrino cross-section on argon and the KDAR muon neutrino cross-section. Their project will also aid in the search for exotic particles beyond the standard model using the five years of data acquired by the MicroBooNE detector.

With this project, researchers will perform large-scale molecular-dynamics (MD) simulations to advance our understanding of chemical separations. Their simulations will provide critical input to ongoing machine learning studies and provide insights to understand experimental results through modeling.

This project will use predictive hierarchical modeling and machine learning to accelerate the discovery and design of materials for a variety of energy-related applications. Their work will improve the understanding and selection of nanoporous materials for separation and catalytic processes in the chemical, biorenewable, and petrochemical industries.

This project aims to support the modern design of high-temperature alloys for automotive propulsion applications. The team's research will fill key knowledge gaps and reduce the time required to move from prototype high-temperature alloy development concepts to real-world deployment.

This project will use DOE supercomputers to develop a detailed model of Fermilab's Proton Improvement Plan (PIP-II) facility's accelerator beamline and infrastructure. Their work will enable comprehensive Monte Carlo studies from the standpoint of radiation shielding including both normal operation and accident scenarios.

This ALCC project will support an effort underway to investigate the conversion of the High Flux Isotope Reactor from a high enriched uranium core to a low enriched uranium core. The team will perform direct numerical simulations of turbulent single- and two-phase flows at an unprecedented level of detail to answer fundamental questions about the interaction and evolution of turbulence within complex geometries.

The team will provide a comprehensive study of the model-dependence of the equation of state of neutron matter, particularly relevant in view of the recent detection of gravitational waves by the LIGO-Virgo collaboration.

With this ALCC project, researchers will use DOE supercomputers to leverage existing organizational relationships, scalable data sources, and unique algorithms to develop nation-scale building energy use models.

This project aims to demonstrate, for the first time, the viability of assessing the long-term properties necessary for the design of new, high-temperature energy generation technologies. To do so, the researchers will carry out a large grid of molecular dynamics calculations spanning several orders of magnitude in strain rate and initial defect density.

This project seeks to accelerate the discovery and deployment of new solar materials for better organic solar cells by combining quantum mechanical simulations with machine learning.

This project will take the next step in demonstrating that staggered valence quarks are a viable strategy in lattice quantum chromodynamics (QCD) for nucleon physics. With this ALCC allocation, the team will compute the nucleon axial charge, a hadronic matrix element entering the neutron decay rate and, simultaneously, the normalization of the nucleon axial form factor.

This project will apply a theoretical framework for predicting the chemistry of complex systems, in both the gas phase and extended phases, that is readily parallelizable and scalable and that leverages high-performance computing. The resulting stochastic a priori dynamics approach is designed to enable predictive discovery in systems with use-inspired complexities.

Researchers will use DOE supercomputers to generate a database of quantum-mechanical data for ground and excited electronic states of gas-phase chemical intermediates. Data will subsequently be used to train a deep neural network reactive force field capable of accurately describing chemical reaction dynamics.

This project will use newly implemented multi-reference quantum Monte Carlo (QMC) methods to provide reference data for parallelized many-body perturbation theory calculations of several molecular sets and explore approaches to improve their accuracy.

Researchers will continue their work to develop novel algorithms for reconstructing x-ray images of thick, real-life materials. Their approach aims to advance the full range of future nanoscale imaging activities, including cell and brain imaging research, at Argonne's Advanced Photon Source and other DOE light sources.