The main objective of this research project is to develop a data-driven operator level surrogate model (DeepONet) to take the place of atomic scale modeling in a time-dependent multiscale system. As noted by DeepMind researcher Irina Higgins, “Once DeepONet is trained, it can be applied to new input functions, thus producing new results substantially faster than numerical solvers.”

There is an increasing worldwide demand for high energy density batteries. This research is aimed at using large-scale, high performance computing to assist the discovery of novel battery electrolytes.

In this project, supercomputers at Oak Ridge Leadership Computing Facility, Argonne Leadership Computing Facility, and the National Energy Research Scientific Computing Center will be used to refine the theoretical prediction based on a technique known as lattice Quantum Chromodynamics (QCD).

In this project, the team aims to develop and leverage cutting-edge ab initio computational methods to investigate the temperature-dependent optical and transport properties of compound semiconductors, perovskites, and plasmonic ceramics for uses in power devices, solar cells, and light-emitting diodes.

Astrophysical collisionless shocks are among the most powerful particle accelerators in the Universe. The objectives of this research are to perform large-scale first-principles simulations of magnetized collisionless shocks to address important longstanding questions associated with energy partition and particle acceleration.

Using Polaris at the Argonne Leadership Computing Facility, this team will compute the structure and spectrum of strongly-coupled hadronic states directly from the fundamental theory describing the interactions of quarks and gluons – the fundamental particles of nuclear matter. These calculations will provide essential theoretical support to the experimental program of the Thomas Jefferson National Accelerator Facility (Jefferson Lab) including the CLAS12 and GlueX experiments, and to the future Electron Ion Collider (EIC) at Brookhaven National Laboratory.

This INCITE project's x-ray burst simulations will provide insight into the rapid proton capture process nucleosynthesis, connect with observations, and probe the structure of the underlying neutron star.

The results from this INCITE project will help answer fundamental questions regarding spontaneous chiral symmetry breaking in strong interactions, flavor symmetry violation, color confinement, and the origin of the mass of hadrons.

With this INCITE project, the researchers aim to integrate a validated multiscale modeling framework to study ion transport kinetics at complex interfaces in solid-state battery and hydrogen storage systems.

With this INCITE project, researchers from the University of Southern California are leveraging leadership-scale quantum dynamics simulations, machine learning, and x-ray free-electron laser (XFEL) experimental data to extend the frontier of ultrafast materials science.

This INCITE Project takes a critical step toward understanding the behavior of black holes in the universe. 

The outcome of this INCITE project will contribute to solving the top global threats in the next decade—extreme weather and climate action failure.

With this INCITE project, researchers will advance the Automatic Building Energy Modeling (AutoBEM) project to extend existing capabilities for creating and simulating a model of every building in America (bit.ly/ModelAmerica) to estimate energy, emissions, and cost reductions of energy-efficient building technologies.

This INCITE project aims to reduce the computational and energetic costs of producing successful peptide macrocycle drugs or industrial enzymes using two approaches.

This INCITE project is aimed at uncovering flow physics, producing validation data for lower-fidelity simulation approaches, and supporting the development of improved predictive theory.

This project aims to predict and better understand the quantum-mechanical properties of materials that display novel properties, including novel quantum phases.

This INCITE project seeks to advance the current state of the art for online data analytics and machine learning (ML) applied to large-scale computational fluid dynamics simulations, as well as to develop more predictive lower-fidelity (and thus less computationally expensive) turbulence models for flows of interest to the aerospace, automotive, and renewable energy industries.

Building on previous INCITE research, this project employs advanced ab initio quantum many-body techniques coupled with applied mathematics and computer science methods to study a wide range of nuclei and to accurately describe the atomic nucleus from first principles.

With this INCITE project, these researchers will be able to conduct large-scale simulations of hypersonic flow fields based on the fundamental interactions of atoms and molecules in the gas.

The project carries out simulations of electronic excited state properties of heterogeneous materials by coupling first-principles molecular dynamics and electronic structure methods beyond density functional theory.