High pressure methods provide a promising route to efficient synthesizing exotic carbon nanoparticles (CNP). For example, it is well established that compressive shockwaves can be used to synthesize a variety of CNP on extremely rapid (e.g., sub us) timescales1,2,3. However, little is understood about the complicated reaction-driven phase separation and phase transformation that underlies this phenomenon – information critical for realizing methods like laser-shock as a tunable CNP synthesis method – due to the extreme and rapidly changing temperatures and pressures that preclude detailed time-resolved experimental characterization2,3,4. Atomic-resolution simulation could provide complimentary insight into this phenomenon, but remains a significant challenge due to the need for “quantum accuracy” on large spatiotemporal scales (e.g., ~102 nm and 101 ns).
In this presentation, we discuss recent progress in deploying machine-learning-accelerated simulations to elucidate the phenomena underlying CNP shock-synthesis. We show that our approach can be used to construct the bulk-equilibrium phase diagram for carbon and provide quantitative insight into the kinetics and mechanisms for phase transformations in carbon at both bulk and finite length scales. We demonstrate how this information can be used to interpret and contextualize related experimental data and show how the emerging picture of CNP formation mechanism is complicated when additional elements are introduced into the system.