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.

Keywords:

• Born–Mayer potential
• Coulomb potential
• Evolutionary algorithms
• Fracture toughness
• Genetic algorithms
• Inverse problem
• Ionic materials
• Lightness
• Materials design
• Multiobjective optimization
• Pareto-problem
• Thermodynamic stability

Notes

Note: 1 Å/pS = 100 m/s.