Interpretable Machine Learning To Accelerate the Analysis of Doping Effect on Li/Ni Exchange in Ni-Rich Layered Oxide Cathodes

In Ni-rich layered oxide cathodes, one effective way to adjust the performance is by introducing dopants to change the degree of Li/Ni exchange. We calculated the formation energy of Li/Ni exchange defects in LiNi0.8Mn0.1X0.1O2 with different doping elements X, using first-principles calculations. We then proposed an interpretable machine learning method combining the Random Forest (RF) model and the Shapley Additive Explanation (SHAP) analysis to accelerate identification of the key factors influencing the formation energy among the complex variables introduced by doping. The valence state of the doping element effectively regulates Li/Ni exchange defects through changing the valence state of Ni and the strength of the superexchange interaction, and COOPSU–SD and MagO were proposed as two indicators to assess superexchange interaction. The volume change also affects the Li/Ni exchange defects, with a larger volume reduction corresponding to fewer Li/Ni exchange defects.