Applications of genetic algorithms in nanomaterials science: a short survey of recent results
Wojciech Paszkowicz
Institute of Physics, Polish Academy of Sciences, al. Lotnikow 32/46, 02-668 Warsaw, Poland.
DOI:
https://doi.org/10.7494/cmms.2013.1.0421
Abstract:
Global-search procedures are an important element of materials design, processing, and properties determination. Genetic algorithms, a subset of algorithms based on evolutionary computation methods, are an example of global optimization methods inspired by biological principles of evolution. In materials science and related fields of science and technology, these algorithms are successfully used, e.g., for optimization of material elaboration and for design of materials with desired physical or structural properties, in working out modern devices based on specific physical principles, as well as in elaboration of improved methods of materials characterization. Nanomaterials science is a rapidly growing subfield of materials science covering various objects characterized by nanometric size. In this review, examples of recent applications of genetic algorithms in nanomaterials science are presented. Representative examples illustrate how useful are such computational methods for solving the scientific tasks, e.g., for thin-film growth modeling and characterization, for optimization of quantum-dot systems and of nanoparticle based medical therapies, for design of hard nanocomposite materials and for optimization of nanomaterial-based optical nanodevices and sensors of various gases.
Cite as:
Paszkowicz, W. (2013). Applications of genetic algorithms in nanomaterials science: a short survey of recent results. Computer Methods in Materials Science, 13(1), 127 – 134. https://doi.org/10.7494/cmms.2013.1.0421
Article (PDF):
Keywords:
Genetic algorithm, Application, Evolution, Optimization, Artificial intelligence, Prediction, Parallel computing
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