• Citation: M. Qamar, M. Mrovec, Y. Lysogorskiy, A. Bochkarev, and R. Drautz (2023), "Atomic Cluster Expansion for Quantum-Accurate Large-Scale Simulations of Carbon", Journal of Chemical Theory and Computation 19(15), 5151–5167. DOI: 10.1021/acs.jctc.2c01149.
    Abstract: 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.

  • LAMMPS pair_style hybrid/overlay pace table linear 10000 (2023--Qamar-M--C--LAMMPS--ipr1)
    See Computed Properties
    Notes: These files were provided by Sergei Starikov on July 15, 2026. The ACE potential that was trained on uncorrected PBE data that does not include van der Waals interactions. A pairwise D2 dispursion correction can be added to the potential using one of the .table files. Use d2_short.table if neighbor distances < 0.2 Å are expected to occur, otherwise use d2.table The link was pulled from the paper and contains training and testing data, and other supporting files.
    File(s): Link(s):
Date Created: October 5, 2010 | Last updated: August 31, 2026