Hydrogen bond energy estimation (H‐BEE) in large molecular clusters: A Python program for quantum chemical investigations - Ahirwar, Mini Bharati; Khire, Subodh S.; Gadre, Shridhar R.; Deshmukh, Milind M. (Journal of computational chemistry, 2024)
A procedure, derived from the fragmentation‐based molecular tailoring approach (MTA), has been proposed and extensively applied by Deshmukh and Gadre for directly estimating the individual hydrogen bond (HB) energies and cooperativity contributions in molecular clusters. However, the manual fragmentation and high computational cost of correlated quantum chemical methods make the application of this method to large molecular clusters quite formidable. In this article, we report an in‐house developed software for automated hydrogen bond energy estimation (H‐BEE) in large molecular clusters. This user‐friendly software is essentially written in Python and executed on a Linux platform with the Gaussian package at the backend. Two approximations to the MTA‐based procedure, viz. the first spherical shell (SS1) and the Fragments‐in‐Fragments (Frags‐in‐Frags), enabling cost‐effective, automated evaluation of HB energies and cooperativity contributions, are also implemented in this software. The software has been extensively tested on a variety of molecular clusters and is expected to be of immense use, especially in conjunction with correlated methods such as MP2, CCSD(T), and so forth. This work reports the automated H‐BEE code for estimating individual hydrogen bond energies and cooperativity contributions in molecular clusters using MTA‐based method employing (i) the actual molecular cluster, (ii) the SS1 model, and (iii) the Frags‐in‐Frags method. This automated code overcomes the tedious manual fragmentation involved in the respective method.