Focusing on ceramic solid-state battery electrolytes and metal hydride hydrogen storage materials, this project integrates three sets of simulation capabilities to predict ion transport kinetics at interfaces.

This project establishes a progressive hierarchy of detailed simulation campaigns of internal combustion engines that will enable scientific discovery and development of predictive models for cycle-to-cycle variability.

This project aims to improve the accuracy and decrease the computational cost of computational methods for designing peptides and proteins for medical and manufacturing applications.

This project investigates how supersonic wall-bounded turbulent flows are affected by the thermal wall boundary condition and how they interact with flexible walls.

This project uses state-of-the-art radiation-hydrodynamics simulations to explore the full physics of supernova explosions.

This project aims to achieve ultrafast control of functional materials via confluence of leadership-scale quantum dynamics simulations, machine learning (ML) and cutting-edge x-ray free- electron laser (XFEL) experiments.

This INCITE project supports the Energy Exascale Earth System Model (E3SM) model, a multi-laboratory project developing a leading-edge climate and Earth system designed to address DOE mission needs.

This project is intended to advance the researchers' first-principles approach, based on real-time time-dependent density functional theory, so as to study electronic stopping processes of complex systems for which going beyond typical-linear response theory formalism is necessary.

Using large-scale simulations based on quantum mechanics, this project tackles two classes of problems: designing (i) sustainable materials to efficiently capture and convert solar energy, and (ii) materials to build novel, optically addressable quantum platforms, including quantum sensors.

The goal of this project is the prediction and understanding of quantum-mechanical properties of materials that display novel properties including novel quantum phases.

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.