journal article·2020-12

Molecular dynamics simulations of a stacked $$\uppi $$-conjugated soft material: binding energy and preferential geometry for self-assembly

Arpita Srivastava, Avinash Garg, Debapratim Das, Ananya Debnath

Bulletin of Materials Science· Volume 43· Issue 1
Publisher page

Abstract

Understanding intermolecular interactions among supramolecular self-assembled organization and identifying the molecular structure relevant to the self-assembly are crucial for designing materials with desired functionalities. Interactions of aromatic rings in a peptide–perylenediimide conjugate (P-1) are investigated using dispersion-corrected density functional theory. The binding energies of fully optimized dimeric P-1 are calculated to identify the most stable conformation of the dimer. We show that the dispersion correction terms have significant contributions to the total energies of the dimers. The combined results from electronic structure calculations and molecular dynamics simulations demonstrate that the stacked dimer with negative inter-planar angle with clock-wise rotation has stronger binding energy than the dimer with positive inter-planar angle. The excess stability of the dimer with clock-wise rotation is attributed to the intra- and inter-molecular $$\uppi $$ – $$\uppi $$ stacking of the side aromatic rings of the dimer facilitated by formation of less number of hydrogen bonds. The stacked P-1 dimer with negative inter-planar angle and stronger binding energy is identified as the building block of a super structure with left-handed helical arrangements. Our calculations will build the first step towards understanding the molecular origin of the stability of a specific super structure of P-1 over the other, as obtained in the experiment relevant to material science and technology.
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Research context

OpenAlex topics: Supramolecular Self-Assembly in Materials · Crystallography and molecular interactions · Luminescence and Fluorescent Materials

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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