Abstract
We study the optimal tuning of the free parameters in range-separated double hybrid functionals, based on enforcing the exact conditions of piecewise linearity and spin constancy. We find that introducing the range separation in both the exchange and the correlation terms allows for the minimization of both fractional charge and fractional spin errors for singlet atoms. The optimal set of parameters is system specific, underlining the importance of the tuning procedure. We test the performance of the resulting optimally tuned functionals for the dissociation curves of diatomic molecules. We find that they recover the correct dissociation curve for the one-electron system, H2+, and improve the dissociation curves of many-electron molecules such as H2 and Li2, but they also yield a nonphysical maximum and only converge to the correct dissociation limit at very large distances.
| Original language | English |
|---|---|
| Pages (from-to) | 2331-2340 |
| Number of pages | 10 |
| Journal | Journal of Chemical Theory and Computation |
| Volume | 18 |
| Issue number | 4 |
| Early online date | 2 Apr 2022 |
| DOIs | |
| Publication status | Published - 12 Apr 2022 |
Funding
This work was supported by the Israel Science Foundation. L.K. thanks the Aryeh and Mintzi Katzman Professorial Chair and the Helen and Martin Kimmel Award for Innovative Investigation. N.G. acknowledges support from the U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences, Chemical Sciences, Geosciences and Biosciences under Award No. KC-030103172684. A portion of the research was performed using EMSL, a DOE Office of Science User Facility sponsored by the Office of Biological and Environmental Research and located at the Pacific Northwest National Laboratory (PNNL). PNNL is operated by Battelle Memorial Institute for the United States Department of Energy under DOE Contract No. DE-AC05-76RL1830. Publisher Copyright: © 2022 The Authors. Published by American Chemical Society and Division of Chemical Education, Inc.
All Science Journal Classification (ASJC) codes
- Computer Science Applications
- Physical and Theoretical Chemistry