Maximizing the energy density and stability of Ni-rich layered cathode materials with multivalent dopants via machine learning

Ni-rich layered cathode materials are promising candidates to satisfy high energy density and high voltage requirements, but they suffer from degradation during cycling. In this study, we developed machine-learning-based surrogate models to predict the average voltage and volume change of Ni-rich cathodes with various dopants (LiNi0.85D’xD’’(0.15 – x)O2) to determine ideal cathode materials with excellent electrochemical properties. To construct the training database, data regarding 4,401 materials were obtained from the Materials Project. Thirty-three elements were implemented as candidate dopants, suggesting 1,617 potential cathode materials. The optimal surrogate models predicting the voltage and volume change displayed R2 values of 0.873 and 0.562 and mean absolute errors of 0.323V and 2.890%, respectively. Using the constructed model, we identified 107 candidate materials with gravimetric energy density of > 875mWh/g, average voltage of > 3.5V and volume change of < 7%. The model was validated using density functional theory calculations. We identified 101 Co-free compounds among the candidates and presented a strategy for material selection that could overcome resource limitations. The constructed platform may be employed to determine ideal Ni-rich cathode materials with different elemental ratios and compositions, with significantly reduced computational and experimental costs.