Using machine learning to screen non-graphite carbon materials based on Na-ion storage properties

Abstract

Non-graphite carbon materials are composed of basic carbon layer units, such as soft carbon, hard carbon, and reduced oxide graphene, and an increasing number of studies on various non-graphite carbon materials are being performed in sodium-ion batteries (SIBs). However, it is difficult to relate the different non-graphite anodes, and a systematic analysis of the correlation between the non-graphite carbon structure and sodium storage properties is lacking. Moreover, there is no strategy to screen for high-performance electrode materials by using the database from the Web of Science. In this study, the effects of crystallinity, an essential attribute of basic microstructural units, on the sodium storage properties have been identified and analyzed. The key structural parameters characterizing the crystallinity were explored. A structure–property database was built based on these parameters (L_a, L_c, d₀₀₂, and I_D/I_G) and the main performance data. The data analysis results were used in conjunction with thermodynamic and kinetic analysis to systematically evaluate the effects of these parameters on the sodium storage performance. Finally, machine learning was used to effectively screen for optimal structural parameters, and a standardized process was proposed for the preparation of high-performance electrode materials programmatically, enabling the continuously updated database to effectively guide the scientific research and engineering application of non-graphite carbon materials.