2026-10-06T17:33:42Z https://api.figshare.com/v2/oai

oai:figshare.com:article/33820525 2026-09-15T19:54:43Z portal_1197 item_type_3 month_year_09_2026

Data for Efficient prediction of nucleus independent chemical shifts for polycyclic aromatic hydrocarbons Dimitrios Kilymis (8873480) Albert P. Bartók (8873483) Chris J. Pickard (1494904) Alexander C. Forse (1304157) Céline Merlet (8873486) Nuclear magnetic resonance Density functionals Quantum chemistry Mathematical physics Polycyclic aromatic hydrocarbons Migrated from ePrints Nuclear Magnetic Resonance (NMR) is one of the most powerful experimental techniques to characterize the structure of molecules and confined liquids. Nevertheless, the complexity of the systems under investigation usually requires complementary computational studies to interpret the NMR results. In this work we focus on polycyclic aromatic hydrocarbons (PAHs), an important class of organic molecules which have been commonly used as simple analogues for the spectroscopic properties of more complex systems, such as porous disordered carbons. We use Density Functional Theory (DFT) to calculate 13C chemical shifts and Nucleus Independent Chemical Shifts (NICS) for 34 PAHs. The results show a clear molecular size dependence of the two quantities, as well as the convergence of the 13C NMR shifts towards the values observed for graphene. We then present two computationally cheap models for the prediction of NICS in simple PAHs. We show that while a simple dipolar model fails to produce accurate values, a perturbative tight-binding approach can be successfully applied for the prediction of NICS in this series of molecules, including some non-planar ones containing 5- and 7-membered rings. This model, one to two orders of magnitude faster than DFT calculations, is very promising and can be further refined in order to study more complex systems.<br><br>Data record consists of a three zip files containing raw data, one containing the Dipolar model, one the TB model, and the results in the third. Input/output files for Gaussian calculations, data sets for all plots shown in the manuscript "Efficient prediction of Nucleus Independent Chemical Shifts for polycyclic aromatic hydrocarbons", C code for the NICS calculations through the dipolar model and python code for the NICS calculations through the tight-binding model described in the manuscript. 2026-09-15T19:54:43Z Dataset Dataset 10.82444/warw.33820525.v1 https://figshare.com/articles/dataset/Data_for_Efficient_prediction_of_nucleus_independent_chemical_shifts_for_polycyclic_aromatic_hydrocarbons/33820525 CC BY 4.0