Computational atomistic blueprinting of novel conducting copolymers using particle swarm optimization

Abstract

A proficient metaheuristic approach viz., particle swarm optimization coupled with negative factor counting technique and inverse iteration method has been employed for designing novel binary and ternary copolymers based on thiophene, pyrrole and furan skeletons. A comparative study of the electronic structures and conduction properties of neutral heterocyclic copolymers and their benzene substituted analogues is inferred using the band structure results derived from ab-initio Hartree-Fock crystal orbital calculations. The band gap value decreases as a result of substitution on the polymer backbone due to increased quinoid contributions which is expected to enhance the intrinsic conductivity of the resulting copolymers. In general, it has been found that HOMO energies have a more decisive influence than LUMO energies on the relative fraction of constituents of the respective low band gap copolymers. The trends in the electronic properties of the respective copolymers are also verified and discussed with the help of density of states. These results can help streamline scrupulous synthetic efforts providing a potent route for molecular engineering of sustainable and efficient electronic materials.

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

“…Lee et al [230] applied an electrical resistance tomography technique combining the PSO algorithm with the Gauss-Newton method, to the visualization of two-phase flows. Thakral and Bakhshi [231] employed PSO with negative factor counting technique and inverse iteration method, for designing novel binary and ternary copolymers based on thiophene, pyrrole, and furan skeletons. Fister et al [232] tried to use the semantic tools such as resource definition framework (RDF) and RDF query language (SPARQL) for the optimization purpose.…”

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