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.

Researchers from TAE Technologies Inc. will use DOE supercomputing resources to provide theory support for the company's existing C-2W fusion plasma experimental device. The project will also contribute to the current effort to design TAE’s next-step fusion device, which will be a reactor-scale prototype designed to demonstrate the ability to achieve fusion relevant conditions.

This project supports the continued development of DOE's Energy Exascale Earth System Model (E3SM), a state-of-the-art fully coupled model of the Earth's climate including important biogeochemical and cryospheric processes. In particular, the team will use DOE supercomputing resources to advance the development, testing, and simulations of the E3SM v2 Cryosphere campaign.

This project will develop data-driven materials-by-design capabilities to accelerate the discovery of new materials for photovoltaic and quantum optical sensing applications. The team will achieve its goal by exploiting the latest advances in materials database auto-generation tools and data-mining, which harness artificial intelligence and machine learning.

Researchers will carry out large-scale simulations of direct-drive Inertial Confinement Fusion (ICF) implosion experiments performed at the OMEGA laser facility. The simultation data will be used to test predictability and identify other possibilities for performance improvement through target or laser modifications.

Researchers will utilize interface capturing methods and direct numerical simulation to perform state-of-the-art, large-scale simulations of reactor flows. Their work aims to help resolve existing challenges in predictive capabilities of two-phase flow, heat transfer, and plasma science.

To support the development of CERN's High Luminosity LHC (HL-LHC) upgrade, researchers will produce simulated data samples that will be highly instrumental in the creation of new reconstruction algorithms for the ATLAS and CMS detectors.

This ALCC project will enable research into experimental and observational data workloads, which differ from the traditional simulation workloads that run at large-scale computational facilities. The team's work will help define the architectural and technical roadmap for experimental and observational facility workflows running at DOE’s HPC facilities, expose the cross-facility policy challenges, and offer strategies to address them.

The discovery of two-dimensional ferromagnetic materials in 2017 ushered in a new era of studies of magnetic order. Using a data-driven approach, this project combines machine learning and high-throughput density functional theory calculations to study van der Waals materials and predict their magnetic and thermodynamic properties.

This project aims to scale up a novel approach to beyond-pure-projection x-ray image construction to meet the challenge of high-resolution x-ray imaging beyond the PPA, benefitting not just cell and brain imaging but the full range of future nanoscale imaging activities at DOE light sources.

Full realization of the goals of multimessenger astrophysics requires the resolution of outstanding computational challenges, which this project seeks to address through the development of algorithms that significantly increase the depth and speed of gravitational wave searches and that process terabyte-size datasets of telescope images in real-time.