Gubler, M., Schäfer, M. R., Behler, J., & Goedecker, S. (2024). Accuracy of Charge Densities in Electronic Structure Calculations. arXiv preprint arXiv:2410.17866.
This research paper investigates the accuracy of charge densities obtained from various DFT exchange-correlation functionals compared to coupled cluster calculations with single and double excitations (CCSD). The study aims to identify the best strategies for obtaining high-precision and converged charge densities for applications like machine learning potentials.
The authors benchmark twelve different DFT functionals and Hartree Fock against CCSD charge densities for a set of small molecules. They use PySCF for CCSD and Gaussian basis set DFT calculations, employing the aug-cc-pV5Z basis set for high accuracy. Six different error measures are calculated, including RMSE of Hirshfeld charges, average maximal point-wise charge difference, average integral over (ρ −ρCCSD)2, average Coulomb energy of ρ −ρCCSD, average infinity norm of difference in dipole moments, and average infinity norm of difference in quadrupole moments.
The study demonstrates that carefully selected DFT functionals can provide charge densities comparable in accuracy to computationally expensive CCSD calculations. This finding has significant implications for applications requiring high-quality charge densities, such as the development of machine learning potentials.
This research provides valuable insights for the computational chemistry community, guiding the selection of appropriate DFT functionals and basis sets for obtaining accurate charge densities. This is particularly relevant for developing and training machine learning models that rely on accurate charge density information.
The study focuses on a limited set of small molecules. Further research could explore the performance of these functionals and basis sets for larger and more complex molecular systems. Additionally, investigating the impact of different integration grids and numerical settings on charge density accuracy could be beneficial.
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