
Quantum-accurate simulation at scale
Billion-atom machine-learning molecular dynamics on DOE exascale supercomputers, with accuracy approaching density functional theory.
Machine-learning potentials →
Oleynik Group · Department of Physics · USF
We explore how matter behaves at extreme pressures and temperatures, inside exoplanets, in fusion implosions and under shock compression, using theory, simulation and experiment.

Billion-atom machine-learning molecular dynamics on DOE exascale supercomputers, with accuracy approaching density functional theory.
Machine-learning potentials →
We lead experimental campaigns at the National Ignition Facility, Sandia's Z machine and the Omega EP laser to test our predictions.
Carbon at extreme conditions →
An inclusive, collaborative team where students and postdocs build technical leadership with cutting-edge research tools.
Meet the team →Rolling admissions for positions starting Summer 2026 or later.

The historic December 5, 2022 experiment at Lawrence Livermore National Lab’s (LLNL) National Ignition Facility (NIF) achieved fusion ignition and paved the way to future …
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Molecular dynamics (MD) simulations have become an essential tool for investigating the physical and chemical behavior of materials under extreme conditions. By predicting the …
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Properties of carbon at extreme pressures and temperatures are of critical importance for constructing interior models of carbon-rich exoplanets. Among the questions that have long …
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The Earth’s core is composed of an iron-rich liquid outer core and a crystalline iron inner core, alloyed with lighter elements, experiencing extreme pressures of up to 360 …
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Recent astrophysical observations have indicated a potential existence of carbide-rich exoplanet. This project that aims to understand the high pressure, high temperature behavior …
Explore →Coverage of our work by OLCF, NVIDIA, LLNL, NSF, APS Physics and Wired.