Predi-XY: a python program for automated generation of NICS-XY-scans based on an additivity scheme

Abstract

Abstract Polycyclic aromatic systems are prevalent in chemistry and materials science because their thermodynamic stability, planarity, and tunable electronic properties make them uniquely suited for various uses. These properties are closely linked to the aromaticity of the systems. Therefore, characterizing the aromatic behavior is useful for designing new functional compounds and understanding their reactivity. NICS-XY-scans are a popular and simple tool for investigating the aromatic trends in polycyclic systems. Herein we present Predi-XY: an automated system for generating NICS-XY-scans for polycyclic aromatic systems using an additivity scheme. The program provides the predicted scans at a fraction of the computational cost of a full quantum mechanical calculation and enables rapid comparison of various polycyclic aromatic systems.