Abstract
New research on Machine Learning is the subject of a report. According to news originating from Shenzhen, People’s Republic of China, by NewsRx correspondents, research stated, “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 LiNiMnXO with different doping elements X, using first-principles calculations.” Our news journalists obtained a quote from the research from Peking University, “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 COOP and Mag were proposed as two indicators to assess superexchange interaction.”
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