Determination of favorable inter-particle interactions for formation of substitutionally ordered solid phases from a binary mixture of oppositely charged colloidal suspensions
This paper introduces the concept of using Artificial Neural Network (ANN) techniques for predicting electrochemical potential of cathode materials in combination with first-principles based quantum mechanical calculations. The proposed method can be used to predict the Lithium ion battery voltage if a new material is chosen as cathode. The methodology has low time-space complexity of computation and aims to integrate ANN with quantum mechanics based Density Functional Theory (DFT) calculations for accelerated insertion of new materials into engineering systems.