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Published November 30, 2023 | Published
Journal Article Open

Multireference Protonation Energetics of a Dimeric Model of Nitrogenase Iron–Sulfur Clusters

Abstract

Characterizing the electronic structure of the iron–sulfur clusters in nitrogenase is necessary to understand their role in the nitrogen fixation process. One challenging task is to determine the protonation state of the intermediates in the nitrogen fixing cycle. Here, we use a dimeric iron–sulfur model to study relative energies of protonation at C, S, or Fe. Using a composite method based on coupled cluster and density matrix renormalization group energetics, we converge the relative energies of four protonated configurations with respect to basis set and correlation level. We find that accurate relative energies require large basis sets as well as a proper treatment of multireference and relativistic effects. We have also tested ten density functional approximations for these systems. Most of them give large errors in their relative energies. The best performing functional in this system is B3LYP, which gives mean absolute and maximum deviations of only 10 and 13 kJ/mol with respect to our correlated wave function estimates, respectively, comparable to the uncertainty in our correlated estimates. Our work provides benchmark results for the calibration of new approximate electronic structure methods and density functionals for these problems.

Copyright and License

© 2023 The Authors. Published by American Chemical Society. Permits the broadest form of re-use including for commercial purposes, provided that author attribution and integrity are maintained (https://creativecommons.org/licenses/by/4.0/).

Acknowledgement

Work at Caltech was supported by the Center for Molecular Magnetic Quantum Materials, an Energy Frontier Research Center funded by the U.S. Department of Energy, Office of Science, Basic Energy Sciences, under Award DE-SC0019330. The computations were conducted at the Resnick High Performance Computing Center, a facility supported by the Resnick Sustainability Institute at the California Institute of Technology. Work at Lund University was supported by grants from the Swedish Research Council (Projects 2018-05003 and 2022-04978). The computations were performed on computer resources provided by the Swedish National Infrastructure for Computing (SNIC) at Lunarc at Lund University and HPC2N at Umeå University, partially funded by the Swedish Research Council (Grant 2018-05973).

Conflict of Interest

The authors declare no competing financial interest.
 

 

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Additional details

Created:
December 6, 2023
Modified:
December 6, 2023