Published March 18, 2021 | Version v1
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GAP interatomic potential for C60

  • 1. Aalto University


This is a Gaussian approximation potential (GAP [1]) for carbon. The potential can be used to model general carbon systems, but is particularly geared towards modeling systems containing C60 molecules and graphitic carbons. It has been fitted with QUIP/GAP [1,2] by recomputing the a-C database of Deringer and Csányi [3] at the PBE level of theory [4] using the VASP code [5,6]. The a-C database from [3] has been augmented significantly to include other atomic structures, namely:

  • Dimers at closer interatomic separation than in [3]
  • Trimers
  • Graphite and graphene (including bilayer graphene) throughout the entire exfoliation curve at different levels of  biaxial strain
  • Glassy carbon
  • Isolated strained and distorted C60 molecules, interacting C60 dimers and colliding C60 dimers
  • Different surface reconstructions of diamond

The main technical novelty introduced in this potential is van der Waals (vdW) corrections at different levels. It incorporates vdW corrections at the Tkatchenko-Scheffler (TS) level of theory [7] via a new machine learning based local parametrization of dispersion interactions. Environment independent ("fixed C6") vdW corrections can also be used (mostly for banchmarking, since the new TS approach is superior). Check the comments in the gap_files/ file for details.

For the underlying PBE fit, this potential uses 2-body (distance_2b) and 3-body (angle_3b) descriptors [3] plus SOAP-type descriptors (soap_turbo) [8,9], as implemented in the TurboGAP code [10]. For the Hirshfeld volume fit, the potential reuses the soap_turbo descriptors for computational efficiency. The files can be used both with QUIP/GAP (compiled with the TurboGAP libraries) and TurboGAP.

The authors acknowledge funding from the Academy of Finland (grants 321713, 329483 and 330488) and computational resources from the Finnish Center for Scientific Computing (CSC) and Aalto University's Science IT project.

More details can be found in this paper:

Machine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of  C60
Heikki Muhli, Xi Chen, Albert P. Bartók, Patricia Hernández-León, Gábor Csányi, Tapio Ala-Nissila, and Miguel A. Caro
Phys. Rev. B 104, 054106 (2021)


  1. A.P. Bartók, M.C. Payne, R. Kondor, and G. Csányi. Phys. Rev. Lett. 104, 136403 (2010).
  2. LibAtoms:
  3. V.L. Deringer and G. Csányi. Phys. Rev. B 95, 094203 (2017).
  4. J.P. Perdew, K. Burke, and M. Ernzerhof. Phys Rev. Lett. 77, 3865 (1996).
  5. VASP:
  6. G. Kresse and J. Furthmüller. Phys. Rev. B 54, 11169 (1996).
  7. A. Tkatchenko and M. Scheffler. Phys. Rev. Lett. 102, 073005 (2009).
  8. A.P. Bartók, R. Kondor, and G. Csányi. Phys. Rev. B 87, 184115 (2013).
  9. M.A. Caro. Phys. Rev. B 100, 024112 (2019).
  10. TurboGAP:


Miguel A. Caro: or


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