A constrained variational approach to the designing of low transport band gap materials: A multiobjective random mutation hill climbing method

Abstract

Neutral polythiophene (PT) and polyselenophene (PSe) are semiconductors with band gaps of about 2 eV. We have proposed and implemented a constrained variational method in which total energy of neutral PT or PSe oligomers is minimized under the constraint that the band gap measured by HOMO-LUMO energy difference is also a minimum in each case. The constrained (bimodal) minimization has been carried out by an adaptive random mutation hill climbing method within the basic framework of Su-Schrieffer-Heeger type of model. We show that the "band-gap constrained minimization" automatically creates electron deficient quinoid regions (QR) in the PT or PSe chains, embedded in aromatic regions (ARs), on both sides. We have investigated how the number and distribution of such QRs can reduce the band gap. Band gap constrained electronic structure calculations thus provide designing clues for low transport band gap materials based on molecular chromophores.

Evolutionary optimization of PAW data-sets for accurate high pressure simulations

“…Thus CARMHC is a smartly coded prescription to quickly generate initial trial solutions for the GA. More details can be found in the cited references. [35][36][37][38] Once the external archive is populated with 2 × n p solution strings (or individuals, or chromosomes), they are fed into a second in-house GA code's initial population and are evaluated for their fitness (Eqs. 7 and 8).…”

“…2), which was previously developed for other optimization problems. [35][36][37][38] The GA starts with a population of potential solutions S = {s} i , i = 1, n p , where n p is the cardinality of the population, that is allowed to undergo a simulated evolution in a sense that in each generation (iteration step) the relatively good solutions are allowed to stay on and reproduce while the bad ones die out, moving the population toward better solutions. The evolution usually starts with a randomly generated population of individuals (candidate solutions).…”

Pure and Hybrid Evolutionary Computing in Global Optimization of Chemical Structures: from Atoms and Molecules to Clusters and Crystals

“…Thus the underlying principle is to randomly choose a solution in the neighborhood of the current solution by means of mutating the string and retain this new solution if it improves the fitness function. A modified Random Mutation Hill Climbing was proposed and implemented by Sarkar et al [56,62,63,64]. The modification lies in additional built-in features for enforcing adaptive control on all the parameters of the search heuristic.…”

“…One area of great promise where EC could decisively prove superior is in designing molecules and materials with targetted properties. Such problems can be cast in the mould of multimodal optimization problem [63] which can be efficiently handled by the EC techniques using for example, the prey-predator model for multi-objective constrained optimization. Alongwith artificial neural network, EA can meet tate-ofthe-art methods for powerful quantitative structure-property relationships modeling [273,274].…”

Single string based global optimizer for geometry optimization in strongly coupled finite clusters: An adaptive mutation-driven strategy

“…In the context of traditional GA we would like to explore the viability of a single string based evolutionary technique, like the random mutation hill climbing method, 35 with additional built-in features for enforcing adaptive control on all the parameters of the search heuristic. There have been proposals to work with partially adaptive analogues of single string based "mutationonly" search heuristics [36][37][38][39][40] to offset, at least partially, the cost of working with a population in serial computation without losing on the ability to locate the GM. A single parent evolutionary algorithm 41 working with novel cutting and pasting operations on a single parent to produce new members has been quite successful in structure optimization.…”