Artificial intelligence driven design of cathode materials for sodium-ion batteries using graph deep learning method

While deep learning has been used in battery computing to speed up the search for new cathode materials, the majority of deep learning techniques only take into account elemental information and topological information, ignoring the significance of geometrical information and global information for electrode average voltage prediction. Multivalent metal-ion Battery Voltage Graph Neural Network (MBVGNN) proposed in present work, which combines global information and geometric information. MBVGNN achieves 43.98 % improvement over reaction-based GATGNN and 47.06 % over TL-CGCNN for the sodium-ion battery dataset. Compared with the best available models, the prediction results for Ca, Zn, Al and Mg electrode materials were improved by 15.50 %, 28.09 %, 44.74 % and 18 %. The formation energy of the cathode material was predicted by MBVGNN, and R2 was 0.969, which was used to evaluate the thermodynamic stability of the cathode material. In addition, density functional theory (DFT) was used to calculate 222 kinds of nickel base sodium-ion battery cathode material's average voltage for high-nickel ternary sodium-ion batteries, and the results were similar with the predicted results. Screening resulted in 194 high-energy-density ternary sodium-ion battery cathode materials. Furthermore, MBVGNN can reliably predict the average voltage of fluorine-substituted layered oxide cathode materials, which may be used to guide the experimental synthesis of these materials. Based on the accuracy of MBVGNN, this research constructs 74,553 high-entropy cathode materials that include layered oxides and three types of poly anions. Through the analysis of the knowledge relationship obtained from the prediction, reference guidance for 16 material types, prioritized elements and 1,369,658 element combinations for the experimental preparation of sodium-ion battery cathode materials is provided. This research employs machine learning methods to demonstrate that the current preparation of high-voltage, high-energy-density cathode materials remains necessary through multiple doping strategies.