Atomic Cluster Expansion for Quantum-Accurate Large-Scale Simulations of Carbon

We present an atomic cluster expansion (ACE) for carbon
that improves
over available classical and machine learning potentials. The ACE
is parametrized from an exhaustive set of important carbon structures
over extended volume and energy ranges, computed using density functional
theory (DFT). Rigorous validation reveals that ACE accurately predicts
a broad range of properties of both crystalline and amorphous carbon
phases while being several orders of magnitude more computationally
efficient than available machine learning models. We demonstrate the
predictive power of ACE on three distinct applications: brittle crack
propagation in diamond, the evolution of amorphous carbon structures
at different densities and quench rates, and the nucleation and growth
of fullerene clusters under high-pressure and high-temperature conditions.