Predicting the Electrochemical Properties of Lithium-Ion Battery Electrode Materials with the Quantum Neural Network Algorithm

Abstract

Discovery of new inorganic solid materials can be accelerated with the aid of a reliable computational tool for predicting the associated electrochemical properties. Hence, we propose a quantitative structure–property relationship model by combining the three-dimensional (3D) quantum mechanical descriptors of materials and the artificial neural network algorithm, which is termed the 3D-QANN model. 3D distribution of electrostatic potentials (ESPs) in the super cell of each inorganic solid material serves as the unique numerical descriptor to derive the 3D-QANN model. The optimized prediction model is then validated in terms of estimating the discharge energy density (D) and the capacity fading (Q) of lithium-ion battery (LIB) cathode materials with the layered structure. The 3D-QANN model reveals good performance in predicting both D and Q values with high correlation with the corresponding experimental data. This indicates the suitability of the quantum mechanical ESP distribution as the numerical descri...