Hydrogen bond energy estimation (H-BEE) in large molecular clusters: A Python program for quantum chemical...

Area of Science:

• Computational chemistry
• Quantum chemistry
• Molecular modeling

Background:

• The fragmentation-based molecular tailoring approach (MTA) estimates hydrogen bond (HB) energies and cooperativity.
• Manual application and high computational costs limit MTA for large molecular clusters.

Purpose of the Study:

• To develop user-friendly software for automated hydrogen bond energy estimation (H-BEE) in large molecular clusters.
• To implement cost-effective approximations for HB energy calculations.

Main Methods:

• Developed in-house software (H-BEE) using Python on a Linux platform with the Gaussian package.
• Implemented two MTA approximations: First Spherical Shell (SS1) and Fragments-in-Fragments (Frags-in-Frags).

Main Results:

• The H-BEE software automates the estimation of individual HB energies and cooperativity contributions.
• The implemented approximations provide cost-effective HB energy evaluations.

Conclusions:

• The H-BEE software significantly enhances the feasibility of applying MTA to large molecular clusters.
• This tool is expected to be valuable for research utilizing correlated quantum chemical methods (e.g., MP2, CCSD(T)).