A program for automatically predicting supramolecular aggregates and its application to urea and porphin

Area of Science:

• Computational chemistry
• Materials science
• Chemical physics

Background:

• Molecular structure and aggregation significantly influence organic material properties.
• Predicting diverse aggregate structures is crucial for theoretical investigations.
• Existing methods may not fully capture the structural diversity of small aggregates.

Purpose of the Study:

• To present EnergyScan, an open-source program for unbiased prediction of geometrically diverse small aggregates.
• To offer a complementary bottom-up approach to existing aggregate prediction methods.
• To demonstrate the utility of EnergyScan in predicting both known and novel aggregate structures.

Main Methods:

• EnergyScan employs a bottom-up approach by scanning the potential energy surface of aggregates.
• Diverse local energy minima are selected from the scanned surface.
• The method was cross-validated using urea dimer and porphin dimer systems.
• Quantum chemistry methods were used to investigate porphin dimers.

Main Results:

• EnergyScan successfully predicted known and novel geometries for the urea dimer.
• A diverse set of porphin dimers was predicted and analyzed.
• Computed transition densities explained observed deviations in absorption spectra for several dimers.
• The program demonstrated its ability to predict aggregates with significant structural and spectral diversity.

Conclusions:

• EnergyScan is an effective tool for the unbiased prediction of geometrically diverse small aggregates.
• The program provides valuable insights into the structure-property relationships of organic materials.
• EnergyScan complements existing theoretical approaches for aggregate studies.