Ab Initio Design of Ni-Rich Cathode Material with Assistance of Machine Learning for High Energy Lithium-Ion Batteries

Abstract

With the widespread use of lithium-ion batteries in electric vehicles, energy storage, and mobile terminals, there is an urgent need to develop cathode materials with specific properties. However, existing material control synthesis routes based on repetitive experiments are often costly and inefficient, which is unsuitable for the broader application of novel materials. The development of machine learning and its combination with materials design offers a potential pathway for optimizing materials. Here, we present a design synthesis paradigm for developing high energy Ni-rich cathodes with thermal/kinetic simulation and propose a coupled image-morphology machine learning model. The paradigm can accurately predict the reaction conditions required for synthesizing cathode precursors with specific morphologies, helping to shorten the experimental duration and costs. After the model-guided design synthesis, cathode materials with different morphological characteristics can be obtained, and the best shows a high discharge capacity of 206 mAh g−1 at 0.1C and 83% capacity retention after 200 cycles. This work provides guidance for designing cathode materials for lithium-ion batteries, which may point the way to a fast and cost-effective direction for controlling the morphology of all types of particles.