The work in this INCITE project takes a critical step toward understanding the behavior of black holes in the universe.

With this INCITE project, the team will perform the first calculations of radiation-dominated accretion on black holes using full transport methods and realistic opacities.

With this INCITE project, researchers are using new advancements in real-time time-dependent density functional theory (RT-TDDFT) to reliably study high-impact scientific questions associated with the dynamics of electrons and ions in complex heterogeneous systems.

This INCITE project will use and further develop methods of grand canonical global optimization for the discovery of dynamic ensembles in realistic reaction conditions and of global activity sampling, for the determination of the most active configurations of the catalyst.

The work in this INCITE project , directly validated byXFEL, UED and neutron experiments at DOE facilities, will enable future production of high-quality custom quantum material architectures for broad and critical applications to continued U.S. leadership in technology development, thereby addressing DOE Basic Research Needs for Transformative Manufacturing and Quantum Materials.

The researchers from this INCITE project will extract structurally and functionally important relationships among genomic elements from experimental data based on physical principles of 3D chromatin folding, and will generate maps of driver interactomes of 3D chromatin folding for each locus along all chromosomes, providing a concise shortened list of putative causal interactions that can drive 3D chromatin folding.

The high-fidelity data generated with this INCITE allocation will reveal the spectral behaviors of turbulent kinetic energy (TKE) as functions of fluid speed, the strength and direction of the magnetic field, and the wall-normal distances.

With this INCITE project, researchers will use very high spatial-resolution regional-scale climate models to explore the physics underlying the formation and evolution of extremes in precipitation and temperature in the current and future climates under various greenhouse gas emission scenarios

This team utilizes large-scale computational tools to help understand how adequately reduced-order formulations can capture the strong coupling between the fluid mechanics of the gas flow and the transport properties of the high-temperature gas.

The approach for this INCITE project is to characterize the effects of pressure gradient and wall cooling on boundary-layer turbulence and perform a thorough evaluation of the existing turbulence models as well as the models that are currently under development.

Coupling machine learning and physics-based methods, with this work researchers aim to accelerate the slow process of drug discovery, which typically lasts many years and costs billions of dollars—a major weakness in public health emergencies.

With this new INCITE project, this team will conduct not only a full suite of 3D simulations for the spectrum of progenitor stars, but plan to double this long-term effort because of code speed-ups and improvements.

With this INCITE project, the team involved has enhanced the particle-in-cell code OSIRIS to launch lasers with arbitrary spatial-temporal profiles.

To advance the design of low enriched uranium fuel elements for future nuclear reactors, researchers are performing high-fidelity simulations of turbulent flows to provide improved engineering predictions and thus more accurate thermal hydraulic safety analysis.

An understanding of the effects of climate change on extreme weather and atmospheric hazards is essential to ascertain future socioeconomic and infrastructural impacts from these events. The awarded effort from this project will produce the world’s first high-resolution “medium” ensemble from a single global modeling system, using regional refinement in the Department of Energy’s recently released Energy Exascale Earth System Model (E3SM) version 2.

In this project, a team of researchers at Argonne National Laboratory plan to use high-fidelity computational fluid dynamics (CFD) and multiphysics simulations to investigate fundamental flow phenomena in next-generation nuclear power reactors.

This team's research will pursue a multi-faceted strategy aimed at modeling short-and-long-range dynamics of nuclei providing reliable estimates of the associated theoretical uncertainty.

This team from Cornell University utilized ALCF supercomputing resources to research innovations that will enable advancements to U.S. wind systems, reduce the cost of electricity, and accelerate the deployment of wind power.

As part of the US Department of Energy (DOE) National Nuclear Security Administration’s (NNSA) initiative to reduce the enrichment of research and test reactors, this research project sets out to investigate the conversion of HFIR from a high enriched uranium (HEU) core to a low enriched uranium (LEU) core.

TAE Technologies combines accelerator physics and plasma physics to solve the challenge of fusion. As part of an ongoing investigation, this team will utilize ALCF HPC resources to conduct first principles particle-in-cell (PIC) simulations to develop an understanding of this newly identified and highly impactful regime