![closeup of molecular simulation](/sites/default/files/styles/front_page_card/public/first-principles-llnl-project-card.png?itok=hVPhZQXs)
First-Principles Molecular Dynamics
This scalable first-principles MD algorithm with O(N) complexity and controllable accuracy is capable of simulating systems that were previously impossible with such accuracy.
![symmetrical geometric shape on a graph](/sites/default/files/styles/front_page_card/public/high-order-llnl-project-card.png?itok=xTEfE-zt)
High-Order Finite Volume Methods
High-resolution finite volume methods are being developed for solving problems in complex phase space geometries, motivated by kinetic models of fusion plasmas.
![compute the electronic structure of atoms, molecules, solids, and liquids within the Density Functional Theory (DFT) formalism](/sites/default/files/styles/front_page_card/public/qbox-llnl-project-card.png?itok=C-tNeIQv)
Qbox
LLNL’s version of Qbox, a first-principles molecular dynamics code, will let researchers accurately calculate bigger systems on supercomputers.
Video: Utilizing HPC to model, simulate, & mitigate wildfire risk
LLNL's Ian Lee joins a Dots and Bridges panel to discuss HPC as a critical resource for data assimilation and numerical weather prediction research.
![map of lower 48 states covered with grid points, plus an inset of Frontier](/sites/default/files/styles/front_page_card/public/2023-08/HiOp-comp-leaderboard_0.png?itok=WHcl4KaT)
Team reaches milestone in power grid optimization on world’s first exascale supercomputer
As part of the Exascale Computing Project’s ExaSGD project, a team including LLNL researchers ran HiOp, an open source optimization solver, on 9,000 nodes of Oak Ridge National Laboratory’s Frontier exascale supercomputer.
![students and mentors strike casual poses in the UCLCC meeting room](/sites/default/files/styles/front_page_card/public/2023-08/DSC-2023-comp-news.png?itok=ynVypMBI)
Data Science Challenge tackles ML-assisted heart modeling
The event brought together 35 University of California students—ranging from undergraduates to graduate-level students from a diversity of majors—to work in groups to solve four key tasks, using actual electrocardiogram data to predict heart health.