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

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 descriptor for LIB cathode materials. Because of the simplicity in model building and the high predictive capability, the 3D-QANN model is anticipated to serve as a useful computational tool for estimating the electrochemical properties and accordingly for designing new materials.