Development of system for prediction of energy density based online machine learning

Abstract

We have developed the scheme for prediction accurate energy density based on the neural network-based batch machine learning by connecting electron density information and energy density. In this presentation, we extended the scheme to the online machine learning, which continuously learns a large amount of data from the electron / energy density database in order to improve the general applicability of the method. It is suggested that the present system based on the online version of the extreme learning machine can predict accurate kinetic / correlation energy densities for arbitrary compounds, which has been difficult in the conventional functionals.