Designing Ionic Materials Through Multiobjective Genetic Algorithms

Abstract

The present work deals with the design of ionic materials as an "inverse problem" where we determine suitable interionic distance to arrive at the desired properties. Specifically, we design ionic materials with high fracture toughness, low density, and high thermodynamic stability. Fracture toughness of the material is determined through molecular dynamics simulations, and the three conflicting objectives are optimized using multiobjective Genetic Algorithms. Two typical lattice systems, namely, the NaCl (B1) structure and the CsCl (B2) structure, are studied. The interionic potential is modeled by a combination of Born-Mayer and Coulomb potentials which represent the electron orbital repulsion and unlike ion attraction, respectively. Attempt has been made to develop a general framework for the design of ionic materials by Genetic Algorithms.

DNA Functionalized Carbon Nanotubes for Nonbiological Applications

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Soft computing techniques in advancement of structural metals

“…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]…”