Computation of Molecular Electronic Structure by Genetic Algorithm

Sharma, Rahul; Saha, Rajendra; Nandy, Subhajit; Bhattacharyya, S.P.; Chaudhury, Pinaki

doi:10.1080/10426910802612197

Cited by 6 publications

(4 citation statements)

References 12 publications

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“…In the field of the electronic structure of atoms, the authors of [36] explore different strategies of GAs with which to solve approximately the Schrödinger equation from the Hamiltonian of a system composed of "N electrons and M nuclei". Thus, in different classes of molecules, in this review, they describe three methods, which they call variational-recipe, direct solution and the density-based approach, whose application they illustrate with different ions.…”

Section: Discussionmentioning

confidence: 99%

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Solving the Schrödinger Equation with Genetic Algorithms: A Practical Approach

Lahoz-Beltrá

2022

Computers

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The Schrödinger equation is one of the most important equations in physics and chemistry and can be solved in the simplest cases by computer numerical methods. Since the beginning of the 1970s, the computer began to be used to solve this equation in elementary quantum systems, and, in the most complex case, a ‘hydrogen-like’ system. Obtaining the solution means finding the wave function, which allows predicting the physical and chemical properties of the quantum system. However, when a quantum system is more complex than a ‘hydrogen-like’ system, we must be satisfied with an approximate solution of the equation. During the last decade, application of algorithms and principles of quantum computation in disciplines other than physics and chemistry, such as biology and artificial intelligence, has led to the search for alternative techniques with which to obtain approximate solutions of the Schrödinger equation. In this work, we review and illustrate the application of genetic algorithms, i.e., stochastic optimization procedures inspired by Darwinian evolution, in elementary quantum systems and in quantum models of artificial intelligence. In this last field, we illustrate with two ‘toy models’ how to solve the Schrödinger equation in an elementary model of a quantum neuron and in the synthesis of quantum circuits controlling the behavior of a Braitenberg vehicle.

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Section: Discussionmentioning

confidence: 99%

“…Step 1-Evaluation of the chromosomes. Using the fitness function (36), we obtain with F(j) the goodness or merit of each chromosome.…”

Section: Genetic Algorithm Implementationmentioning

confidence: 99%

Solving the Schrödinger Equation with Genetic Algorithms: A Practical Approach

Lahoz-Beltrá

2022

Computers

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“…We note here that evolutionary algorithms [12] in general and genetic algorithms [13] in particular have, of late found considerable use in electronic structure calculations of large molecules or clusters [14][15][16][17][18]. However, multiobjective optimization (MOO) based on evolutionary computing method has scarcely been attempted in the present context.…”

Section: Introductionmentioning

confidence: 99%

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

Sarkar

Sharma

Bhattacharyya

2011

Int J of Quantum Chemistry

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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.

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“…The scope and flexibility inherent in the soft computing techniques for solving problems in wide range of areas have been comprehensively discussed in articles and books dealing with applications of ANN [8][9][10][11] and genetic algorithm (GA) 12 in materials research in general, applications of ANN in polymer composites, 13 applications of GA in polymer design and processing, 14 as well as in steel manufacturing. 15 Streams of such activities during the last decade include atomic clusters and crystal structure related issues, [16][17][18][19][20][21][22][23][24][25][26][27][28][29] ceramics, [30][31][32][33][34][35][36][37][38][39] polymers, [40][41][42][43][44][45][46][47] semiconductors [48][49][50][51][52][53]…”

Section: Introductionmentioning

confidence: 99%

Soft computing techniques in advancement of structural metals

Datta

Chattopadhyay

2013

International Materials Reviews

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Current trends in the progress of technology demand availability of materials resources ahead of the advancing fronts of the application areas. During the last couple of decades, significant progress has been made in computational and experimental design of materials. Among the potential computational techniques, soft computing stands in distinction due to the inherent flexibility in capturing the complexity of the problem in global scale. Since 1990s remarkable success has been achieved in soft computing activities in different facets of materials science and engineering. Extensive efforts have been devoted in design of metals and alloys based on composition-process-microstructure-property correlation. The present review aims to address the contribution of soft computing in the field of structural metals and alloys including processing and joining. The critical issues concerning applicability of particular techniques in specific materials problem have been particularly emphasised encompassing the scope of integrating the gradual progress in different techniques in hybrid and tandem framework to address greater complexities in larger length and time scale. Attempt has also been made to emphasise on the evolution of newer knowledge and materials through soft computing activities. Finally, the potential of soft computing techniques in futuristic design approaches has been critically enumerated.

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Computation of Molecular Electronic Structure by Genetic Algorithm

Cited by 6 publications

References 12 publications

Solving the Schrödinger Equation with Genetic Algorithms: A Practical Approach

Solving the Schrödinger Equation with Genetic Algorithms: A Practical Approach

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

Soft computing techniques in advancement of structural metals

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