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

Kravvaris

Computing Atomic Nuclei: Exploring the Universe Through a Nuclear Lens

Ludwig

GPU-based particle-in-cell modeling of nanostructured laser driven targets in the high intensity short pulse regime.

Oppelstrup

Demonstrating dislocation starvation by grain boundaries

Vranas

The Dark Properties of Dark Matter that Shape the Cosmos

Tier 2

Allen

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

Berry

A Quantitative and Experimentally Supported Multiscale Modeling Framework for Protein Condensation Underlying Neurodegeneration

Bertin

Direct Comparison of Metal Strength Between Quantum Accurate MD Simulations and High-strain Rate Experiments

Carpenter

A Virtual Membrane Validated with Precision Experiments

Correa

Simulation of Ultrafast Phase Transitions with Electronic Dissipation

Dimits

Implicitly Coupled Turbulence and Transport Time-Scale Simulations of Tokamak Fusion Reactors

Djordjevic

Exploring the Transition to Exotic Acceleration Regimes of Laser-driven Particles Via Deep Learning

Draeger

Predicting Cell Margination Using Novel, Adaptive, Multi-physics Modeling

Golovich

A New Asteroid Detection Method for Planetary Defense and Solar System Science

Lightstone

Pan-active Therapeutics for Virus Families with Pandemic Potential

McMahon

Kinetic Modeling of Z Pinch Devices for Improved Conversion Efficiency into Beam Energy

Pitarka

Simulations and Modeling of Seismic and Acoustic Waves Applied to Nuclear Explosion Monitoring and Seismic Hazard Assessment

Schunck

High-fidelity Fission Simulations

Zhang Revealing RAS-driven Cancer Initiation through Massive Multiphysics Simulations
Zhou Accelerated Materials Simulations with Graph Neural Networks