AI Summary & Key Takeaways
AI InsightThe results show that the outputs of all optimization methods converge to ideal values with sufficiently long iterations and at different trials.
📌 3 Key Takeaways
Abstract
In order to characterize thermal dependent physical properties of materials, potentially to be used in technological applications, an accurate interatomic-potential parameter set is a must. In general, conjugate-gradient methods and more recently, metaheuristics such as genetic algorithms are employed in determining these interatomic potentials, however, especially the use of metaheuristics specifically designed for optimization of real valued problems such as particle swarm and evaluation strategies are limited in the mentioned problem. In addition, some of these parameters are conflicting in nature, for which multi objective optimization procedures have a great potential for better understanding of these conflicts. In this respect, we aim to present a widely used interatomic potential parameter set, the Stillinger–Weber potential, obtained through three different optimization methods (particle swarm optimization, PSO, covariance matrix adaptation evolution strategies, CMA-ES, and non-dominated sorting genetic algorithm, NSGA-III) for two-dimensional materials MoS2, WS2, WSe2, and MoSe2. These two-dimensional transition metal dichalcogenides are considered as a case mainly due to their potential in a variety of promising technologies for next generation flexible and low-power nanoelectronics, (such as photonics, valleytronics, sensing, energy storage, and optoelectronic devices) as well as their excellent physical properties (such as electrical, mechanical, thermal, and optical properties) different from those of their bulk counterparts. The results show that the outputs of all optimization methods converge to ideal values with sufficiently long iterations and at different trials. However, when we consider the results of the statistical analyses of different trials under similar conditions, we observe that the method with the lowest error rate is the CMA-ES.