Benchmarking Semiempirical Methods To Compute Electrochemical Formal Potentials
Abstract
Computational methods to predict and tune electrochemical redox potentials are important for the development of energy technologies. Here, we benchmark several semiempirical models to compute reduction potentials of organic molecules, comparing approaches based on (1) energy differences between the S 0 and one-electron-reduced D 0 states of the isolated molecules and (2) an orbital energy shift approach based on tuning the charge-transfer triplet energy of the Ag 20-molecule complex; the second model enables explicit modeling of electrode–molecule interactions. For molecules in solution, the two models yield nearly identical results. Both PM7 and PM6 predict formal potentials with only a slight loss of accuracy compared to standard density functional theory models, and the results are robust across several choices of geometries and implicit solvent models. PM6 and PM7 show dramatically improved accuracy over older semiempirical Hamiltonians (MNDO, AM1, PM3, and INDO/S). However, our recently developed INDO parameters model the electronic properties of our Ag 20 model electrode much more accurately than does PM7. These results demonstrate the need for further development of semiempirical models to accurately model molecules on surfaces.
- Publication Date:
- Research Org.:
- Northwestern Univ., Evanston, IL (United States)
- Sponsoring Org.:
- USDOE Office of Science (SC), Basic Energy Sciences (BES) (SC-22)
- OSTI Identifier:
- 1464278
- Alternate Identifier(s):
- OSTI ID: 1508603
- Grant/Contract Number:
- SC0004752
- Resource Type:
- Published Article
- Journal Name:
- Journal of Physical Chemistry. A, Molecules, Spectroscopy, Kinetics, Environment, and General Theory
- Additional Journal Information:
- Journal Volume: 122; Journal Issue: 33; Journal ID: ISSN 1089-5639
- Publisher:
- American Chemical Society
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CHEMISTRY; 97 MATHEMATICS AND COMPUTING
Citation Formats
Gieseking, Rebecca L. M., Ratner, Mark A., and Schatz, George C. Benchmarking Semiempirical Methods To Compute Electrochemical Formal Potentials. United States: N. p., 2018.
Web. doi:10.1021/acs.jpca.8b05143.
Gieseking, Rebecca L. M., Ratner, Mark A., & Schatz, George C. Benchmarking Semiempirical Methods To Compute Electrochemical Formal Potentials. United States. doi:10.1021/acs.jpca.8b05143.
Gieseking, Rebecca L. M., Ratner, Mark A., and Schatz, George C. Thu .
"Benchmarking Semiempirical Methods To Compute Electrochemical Formal Potentials". United States. doi:10.1021/acs.jpca.8b05143.
@article{osti_1464278,
title = {Benchmarking Semiempirical Methods To Compute Electrochemical Formal Potentials},
author = {Gieseking, Rebecca L. M. and Ratner, Mark A. and Schatz, George C.},
abstractNote = {Computational methods to predict and tune electrochemical redox potentials are important for the development of energy technologies. Here, we benchmark several semiempirical models to compute reduction potentials of organic molecules, comparing approaches based on (1) energy differences between the S0 and one-electron-reduced D0 states of the isolated molecules and (2) an orbital energy shift approach based on tuning the charge-transfer triplet energy of the Ag20-molecule complex; the second model enables explicit modeling of electrode–molecule interactions. For molecules in solution, the two models yield nearly identical results. Both PM7 and PM6 predict formal potentials with only a slight loss of accuracy compared to standard density functional theory models, and the results are robust across several choices of geometries and implicit solvent models. PM6 and PM7 show dramatically improved accuracy over older semiempirical Hamiltonians (MNDO, AM1, PM3, and INDO/S). However, our recently developed INDO parameters model the electronic properties of our Ag20 model electrode much more accurately than does PM7. These results demonstrate the need for further development of semiempirical models to accurately model molecules on surfaces.},
doi = {10.1021/acs.jpca.8b05143},
journal = {Journal of Physical Chemistry. A, Molecules, Spectroscopy, Kinetics, Environment, and General Theory},
number = 33,
volume = 122,
place = {United States},
year = {2018},
month = {7}
}
DOI: 10.1021/acs.jpca.8b05143
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