Machine learning models for predicting density of sodium-ion battery materials

Abstract

With the unprecedented amounts of material data generated from high-throughput density functional theory, machine learning provides the ability to accelerate the discovery and design of new materials. In this work, machine learning regression techniques are applied to a large amount of data from Materials Project Database, to develop machine learning models capable of accurately predicting the densities of sodium-ion battery cathode materials. Different machine learning regression models are successfully developed and validated. Feature vectors derived from the properties of materials’ chemical compounds are evaluated. Extra trees regressor model is found to be the best model in predicting the density with an accuracy of 0.95 and 0.09 g/cm3 coefficient of determination and mean square error, respectively.