Published October 30, 2024 | Version v1
Dataset Open

General purpose Gaussian approximation potential for P with Hirshfeld volumes for vdW corrections

  • 1. ROR icon Aalto University

Description

This is a Gaussian approximation potential (GAP [1]) for phosphorus. The potential can be used to model general phosphorus systems, including the various solid phases as well as molecular and polymeric fluids. It has been fitted with gap_fit [2] by recomputing the database of Ref. [3] at the PBE level of theory [4] using the VASP code [5,6].

The potential allows for adding van der Waals (vdW) corrections at the Tkatchenko-Scheffler (TS) [7] and many-body-dispersion (MBD) [8] levels via machine learning based local parametrization of dispersion interactions [9] and the linear-scaling MBD (lMBD) approximation [10], respectively.

For the underlying PBE fit, this potential uses 2-body (distance_2b) and SOAP-type descriptors (soap_turbo) [11,12], as implemented in the TurboGAP code [13]. For the Hirshfeld volume fit, the potential reuses the soap_turbo descriptors for computational efficiency. The files can be used both with QUIP/GAP (without vdW-correction support) and TurboGAP (with full functionality).

The reference for the implementation paper will be added here later.

Funding and resources

The authors acknowledge funding from the Academy of Finland (grants 321713, 330488 and 347252) and the European Union: Innovation Study XCALE has received funding through the Inno4scale project, which is funded by the European High-Performance Computing Joint Undertaking (JU) under Grant Agreement No 101118139. The
JU receives support from the European Union's Horizon Europe Programme. Computational resources from the Finnish Center for Scientific Computing (CSC) and Aalto University's Science IT project are also acknowledged.

References

  1. A.P. Bartók, M.C. Payne, R. Kondor, and G. Csányi. Phys. Rev. Lett. 104, 136403 (2010).
  2. S. Klawohn, J.P. Darby, J.R. Kermode, G. Csányi, M.A. Caro, and A.P. Bartók. J. Chem. Phys. 159, 174108 (2023).
  3. V.L. Deringer, M. A. Caro, and G. Csányi. Nat. Commun. 11, 1 (2020).
  4. J.P. Perdew, K. Burke, and M. Ernzerhof. Phys Rev. Lett. 77, 3865 (1996).
  5. VASP: http://vasp.at
  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. Tkatchenko, R. A. DiStasio Jr, R. Car, and M. Scheffler. Phys. Rev. Lett. 108, 236402 (2012).
  9. H. Muhli, X. Chen, A.P. Bartók, P. Hernández-León, G. Csányi, T. Ala-Nissila, and M.A. Caro. Phys. Rev. B 104, 054106 (2021).
  10. H. Muhli, T. Ala-Nissila, and M.A. Caro. arXiv preprint arXiv:2407.06409 (2024).
  11. A.P. Bartók, R. Kondor, and G. Csányi. Phys. Rev. B 87, 184115 (2013).
  12. M.A. Caro. Phys. Rev. B 100, 024112 (2019).
  13. TurboGAP: http://turbogap.fi

Contact

Miguel A. Caro: mcaroba@gmail.com or miguel.caro@aalto.fi

Files

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Additional details

Funding

Towards accurate computational experimentation (COMPEX): machine-learning-driven simulation of nanocarbon synthesis 321713
Research Council of Finland
Next-generation interatomic potentials to simulate new cellulose-based materials (NEXTCELL) 330488
Research Council of Finland
Exascale-ready machine learning force fields / Consortium: ExaFF 347252
Research Council of Finland
Inno4Scale – Innovative Algorithms for Applications on European Exascale Supercomputers 101118139
European Commission