Grand Challenge Awardees

The Computing Grand Challenge Program allocates significant quantities of institutional computational resources to LLNL researchers to perform cutting edge research on the LC capability computers. Proposals are reviewed by a lab-wide committee of researchers on the basis of the expected visibility and impact of the computational approach and anticipated scientific results. Most projects selected for Grand Challenge are awarded Tier 2 allocations, which corresponds to a substantial allocation on LC capability machines. A small number of highly-scored proposals are awarded larger Tier 1 allocations each year to showcase the highest visibility computational science currently being conducted at LLNL.

Awardees by award period

Tier 1

Bertin

Unraveling Dislocation Multiplication with Molecular Dynamics simulations

Carpenter

Ft pathogen countermeasure design using machine learning and membrane modeling

Kravvaris

Computing Atomic Nuclei Exploring the Universe Through a Nuclear Lens

McGill

The Next Generation of Exoplanet Atmospheric Models for the
James Webb Space Telescope and Beyond

Vranas

Strongly Interacting Composite Particle Scattering with Machine Learning

Tier 2

Allen

Using Machine Learning and Molecular Mechanics Calculations for Biological and Chemical Weapons Countermeasures

Correa

Nonperturbative Studies Of Materials Under Laser-induced
Nonequilibrium Conditions

Djordjević

Enhancing subscale modeling of kinetics in high-energy-density plasmas with deep learning

Draeger

Developing Vascular Digital Twins for Diagnostics, Remote
Monitoring, and Virtual Interventions

Frolov

Cracking in additively manufactured tungsten

Ji

Accelerated Phase-Field Modeling of Microstructure Development in Rapid Alloy Solidification

Kemp

Modeling relativistic picosecond laser interaction with micro-structured targets at scale with three-dimensional Particle-in-Cell simulations

Li

Predicting performance and oxidative stability of amine-functionalized polymers for carbon capture

Ludwig

Modeling of Laser Wakefield Accelerator Experiments on the Next Generation of Lasers for Muon Imaging

Ozturk Dalpe

Molecular dynamics simulations of the wild-type and mutant muscle motor proteins In support of the LDRD LW: A faster way to make muscle motor proteins using muscle cell lysate

Pei

High Performance Computing-Aided Drug Discovery

Pham

Advancing Nanostructured Electrocatalysts through Machine Learning-Enhanced Molecular Dynamics Simulations

Pitarka

Improvements of SW4 Seismic Code and Simulation of Seismic Sources for National Security and Earthquake Hazard Assessment

Pottier

Machine Learning Driven Multiscale Simulations of RAS/RAF Activation

Riedel

Transport and coupling of short-pulse laser-driven particle beams in the fast ignition approach to inertial fusion energy

Schunck

High-fidelity Fission Simulations for Astrophysics

Stuck

Testing new turbulence models with Direct Numerical Simulations of turbulent magnetohydrodynamic channel flow