The main objective of this work is to design structural materials by interatomic potential based methods and to explore materials of high strength to weight ratio with a thermodynamically stable structure. The idea is to develop a material that is simultaneously, stiff , light and at the same time thermodynamically stable. Since these requirements are conflicting to each other, it leads to a Pareto-optimal problem, which in this study was computed through the rather well known NSGA II Algorithm. Proper quantification was made for the three objectives, stiffness, lightness and energy of the system. Attempts were made to maximize the first two while the third was minimized. The three objectives were optimized for simple cubic, body-centered cubic, face-centered cubic crystal lattices and also for the amorphous atomic clusters. For the crystalline materials simulations were done keeping the lattice parameters as variables, which were allowed to evolve through a genetic procedure. In case of the atomic clusters the individual atomic coordinates were allowed to vary without any special regards for a specific lattice structure, as it has been done in the crystalline cases. In all the simulations the material behavior was modeled using the Lennard-Jones potential function. The Pareto frontier obtained in this study would lead to a series of hypothetical materials, each with its own lattice parameters which, in terms of the three objectives considered in this study would be non-dominating to each other, thus representing the best possible compromises between the objective functions. The solutions obtained after achieving the Pareto-optimality are individually examined and their properties are compared and contrasted with the existing materials, as and when possible. A typical Pareto front is shown in the attached figure.

• Legal notice:
  

  Copyright (c) Pielaszek Research, all rights reserved.
  The above materials, including auxiliary resources, are subject to Publisher's copyright and the Author(s) intellectual rights. Without limiting Author(s) rights under respective Copyright Transfer Agreement, no part of the above documents may be reproduced without the express written permission of Pielaszek Research, the Publisher. Express permission from the Author(s) is required to use the above materials for academic purposes, such as lectures or scientific presentations.
  In every case, proper references including Author(s) name(s) and URL of this webpage: https://science24.com/paper/11985 must be provided.

1. Genetic algorithms: Explanation of basic ideas (tutorial lecture part I)
2. Genetic algorithms: Multi-objective methods (tutorial lecture part II)
3. The genetic algorithm-neural net combination (tutorial lecture part III)
4. Genetic algorithm based search on the role of variables in the work hardening process of multiphase steels
5. Modeling of recrystallization in cold rolled copper using inverse cellular automata and genetic algorithms
6. Genetic algorithm based multi-objective optimization of an ironmaking rotary kiln