Atomistic simulation and evolutionary optimization of Fe-Cr nanoparticles

ABSTRACT

Iron-chromium nanoparticles have some very important real-life applications, for example in trapping the He bubbles in the nuclear reactors or as catalysts in organic reactions, e.g. the reduction of substituted aromatic ketones to alcohol. The only parameter which can be tweaked in Iron nanoparticles is their size. Once it is fixed, the properties of the particle are fixed. The addition of chromium implies there is more parameter to tune (along with the particle size), i.e. the concentration of chromium in the nanoparticle. Molecular dynamics is used to simulate the particle system, calculate the required parameters (average cohesive energy and average surface energy) for them under both static and dynamic loading conditions and then using the evolutionary data-driven modeling is used to optimize the particle parameters for finding out the best feasible parameters, that lead to the generation of a stable nanoparticle with properties suitable for practical applications. We find that using molecular dynamics along with a variety of evolutionary data-driven optimization algorithms provides the desired results, i.e., a variety of Fe-Cr nanoparticles with a range of diameters and compositions to choose from, in order to design alloys or catalysts with high surface energies and low cohesive energies.

KEYWORDS:

• Fe-Cr nanoparticles
• catalysis
• atomistic simulation
• multi-objective optimization
• genetic algorithm

Acknowledgments

The authors extend their gratitude to Abinash Senapati and Harshit Agarwal for their valuable contribution at an early stage of this work.