摘要：= 0.833, from the extremely randomized tree with adaptive boosting algorithm. Furthermore, we propose a reverse engineering framework to search for experimental parameters that satisfy the target electrochemical specification. The proposed results were validated by experiments. The current results demonstrate that machine learning has great potential to accelerate the optimization process for the commercialization of cathode materials.
