Generalized Image Charge Method to Calculate Electrostatic Magnitudes at the Nanoscale Powered by Artificial Neural Networks

Abstract

A technique to calculate electrostatic magnitudes such as force and potential in Electrostatic Force Microscopy setups is presented. This technique combines Artificial Neural Networks and the Generalized Image Charge Method to overcome one of the main problems of traditional numerical simulations: the need of many parameters that are difficult to estimate and depend on the geometry of the experimental setup. Using Artificial Neural Networks, our technique is able to estimate the internal parameters of the algorithm and automatically obtain the electric magnitudes with a very high accuracy. This technique has been implemented in the freely distributed software winGICM. The automatic configuration of the software by an Artificial Neural Network allows the users to handle it without being specifically trained in the theoretical background underlying the algorithms.