A simulation tool for evolving functionalities in disordered nanoparticle networks

Recently published experimental work on evolution-in-materio applied to nanoscale materials shows promising results for future reconfigurable devices. These experimental results are based on disordered nanoparticle networks, without a predefined design. The material is treated as a black-box, and genetic algorithms are used to find appropriate configuration voltages to enable a targeted functionality. To support future experimental work, we developed simulation tools for predicting candidate functionalities. One of these tools is based on a neural network model, but the one presented here is based on a physical model. The physical model describes the charge transport between the nanoparticles, which is governed by what is known as the Coulomb blockade effect. The new simulation tool combines a genetic algorithm with Monte-Carlo simulations that are based on this physical model. The code of the new simulation tool has been validated with known results on small deterministically designed nanoparticle networks from literature. The code has also been applied to simulate reconfigurable logic in small k×k grids of nanoparticles. The results show that the new approach has great potential for partly replacing costly and time-consuming experiments.