This INCITE project seeks to create a direct numerical simulation (DNS) dataset capturing all the microscale processes involved in hypersonic boundary layer transition, so as to inform passive control techniques that reduce the drag and aerodynamic heating experienced in hypersonic flight.

This INCITE project seeks to address the major challenges facing cellular simulations to allow cancer researchers to quickly identify potentially deleterious mechano-phenotypes. 

This project aims to build foundation models for large-scale genomic datasets for continuous monitoring and tracking of pathogens. It will thus increase biopreparedness and will benefit the community by making GenSLM models, data, and code available to a broad user base, who can fine-tune the foundation models for their own downstream predictive tasks.

This project aims to leverage both traditional supercomputers and quantum computers to make computational drug design more efficient.

This team of researchers will carry out direct numerical simulations to study and quantify turbulence kinetic energy and diffusive scalar fields in gravity-driven turbulent bubbly suspensions.

This INCITE project helps to meet the challenges of reducing energy, realizing new technologies, and identifying the optimum materials for specific applications.

This project will advance the current state of the art for online data analytics and machine learning applied to large-scale computational fluid dynamics (CFD) simulations to develop enhanced turbulence models for flows of interest to the aerospace, automotive, and renewable energy industries.

This project will usher in a new era of cosmological simulations by fully exploiting the power of DOE’s exascale systems, providing scientific results that will be a critical input for ongoing and upcoming cosmological surveys.

This project will advance our understanding of nuclear phenomena by targeting predictive capabilities regarding structure and reactions of nuclei, fundamental symmetries, and neutrino and electron interactions in nuclei.

This work aims to determine the properties of strongly interacting matter under extreme conditions from numerical simulations of the early universe, experimental heavy ion collisions, and compact stars.

This INCITE proposal aims to produce datasets of human brain connectivity at unprecedented scale for analysis within a separately funded neuroscience-driven project, and to publish the data via ALCF’s Globus- based data sharing facilities. 

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.