This team set out to perform an unprecedented pair of decadal-scale climate simulations with an atmospheric grid spacing of 3.25 km. 

The team will use a sizeable INCITE allocation to explore efficient alternatives for transformer models for language modeling.

This project will calculate the 3D structures of the pion and kaon, which are the Nambu-Goldstone bosons of the strong force that bind nuclei together, to advance our understanding of the strong interaction and confinement of quarks and gluons inside hadrons.

This project will scale up the training of foundation models to the largest available chemical libraries to advance the design of new electrolytes for energy storage and energy conversion applications.

This project uses a new performance-portable version of the Athena++ astrophysical MHD code to perform the first calculations of radiation-dominated accretion on black holes using full transport methods and realistic opacities.

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.