Please use this topic to ask any questions related to Q-Chem Webinar 70 (presented by Jonathan Wong on July 27, 2023).
Abstract
Molecular property calculations have long been a cornerstone of computational chemistry, providing invaluable insights for experimental work. Among these properties, nuclear magnetic resonance (NMR) shieldings play a crucial role in characterizing chemical environments. Conventionally, these calculations rely on the often tedious derivation and implementation of the analytical form of the energy derivative. On the other hand, the energy derivative can be carried out in a numerical manner. In this webinar, I will present a method-independent, fully numerical finite difference approach for NMR shielding calculations, utilizing gauge-including atomic orbitals (GIAO), that is recently implemented in Q-Chem. This effectively leverages the abundant quantum chemistry methods in Q-Chem, without the need for analytical derivative implementation.
In the first part, I will introduce the theory underlying our Q-Chem implementation of the GIAO calculations, including the new matrix elements required, fully complex SCF and post-SCF calculations, and finally the numerical derivative. In the second part, I will present proof-of-concept results with κ-regularized MP2 and MP2.X, where a variable fraction X of third-order correlation (MP3) is incorporated. Results show significant improvements with κ-MP2 for 13C and 15N shieldings. Surprisingly, MP2.6 outperforms CCSD for all heavy nuclei, indicating potential renormalization of double amplitudes to account for neglected triple substitutions. Lastly, I will use a few examples to demonstrate how to use this new module in Q-Chem, as well as exciting ongoing developments.
About the Presenter
Jonathan Wong received his B.A. in Chemistry from Cornell University in 2017 and his PhD in Chemistry from the University of California Berkeley in 2023, advised by Prof. Martin Head-Gordon. He is currently working as a researcher at AIMdyn Inc, exploring designer AI and its computational chemistry applications.