Periodic GFN1-xTB Tight Binding: A Generalized Ewald Partitioning Scheme for the Klopman–Ohno Function
A novel formulation is presented for the treatment of electrostatics in the periodic GFN1-xTB tight-binding model. Periodic GFN1-xTB is hindered by the functional form of the second-order electrostatics, which only recovers Coulombic behavior at large interatomic distances and lacks a closed-form solution for its Fourier transform. We address this by introducing a binomial expansion of the Klopman-Ohno function to partition short- and long-range interactions, enabling the use of a generalized Ewald summation for the solution of the electrostatic energy. This approach is general and is applicable to any damped potential of the form |<i>R</i><sup><i>n</i></sup> + <i>c</i>|<sup>-<i>m</i></sup>. Benchmarks on the X23 molecular crystal dataset and a range of prototypical bulk semiconductors demonstrate that this systematic treatment of the electrostatics eliminates unphysical behavior in the equation of state curves. In the bulk systems studied, we observe a mean absolute error in total energy of 35 meV/atom, comparable to the machine-learned universal force field, M3GNet, and sufficiently precise for structure relaxation. These results highlight the promising potential of GFN1-xTB as a universal tight-binding parametrization.
Authors
- Alexander Buccheri (ORCID: https://orcid.org/0000-0001-5983-8631)
- J. Emiliano Deustua (ORCID: https://orcid.org/0000-0001-8193-2229)
- Frederick R. Manby (ORCID: https://orcid.org/0000-0001-7611-714X)
- Seyed Mohamad Moosavi (ORCID: https://orcid.org/0000-0002-0357-5729)
- Peter J. Bygrave (ORCID: https://orcid.org/0000-0002-5505-5637)
- Rui Li (ORCID: https://orcid.org/0009-0008-9128-8500)
Institutions
- California Institute of Technology (US)
- University of Toronto (CA)
- University of Bristol (GB)
- Division of Chemistry (US)
- Max Planck Institute for the Structure and Dynamics of Matter (DE)
Publication Details
- Journal
- Journal of Chemical Theory and Computation
- Published
- 2025-02-05
- DOI
- https://doi.org/10.1021/acs.jctc.4c01234
- Citations
- 5
- Primary Topic
- Machine Learning in Materials Science
- Type
- article
- Field-Weighted Citation Impact
- 2.59
Funders
- Max-Planck-Gesellschaft
- Engineering and Physical Sciences Research Council
- Basic Energy Sciences