Metaheuristic ab initio optimum search for doping effects in nanocarbons

We have developed a combined approach of metaheuristic optimization algorithms (MOA), such as the genetic algorithm, with an ab-initio materials simulation engine. Concurrent run of the ab-initio calculations with each different parameter set selected by the MOA searches the optimum condition within a given input-parameter space. Using this methodology, the optimum dopant and its position/structure at a graphene edge are found to be a multiple N-atoms doping at graphitic sites, which predicts to lead to better charging/discharging performance when it is used as an anode material of Li-ion battery.