Accelerated Exploration of Li-Rich Cathode Materials with High Energy Density for Li-Ion Batteries
Authors: Esaki Shogo , Natsui Ryuichi , Matsushita Junko , Ikeuchi Issei , Hibino Mitsuhiro , Hagawa Reiko , Imade Masahiro , Nakura Kensuke 
Published: August 2024

Li-rich cathode materials have been widely researched as promising cathode materials for Li-ion batteries due to the abundant numbers of Li+ in their formula units. There are, however, still some problems, such as the fading discharge voltage during cycling, to be overcome before its commercial application can be realized. In recent years, there has been a global expansion of efforts to innovate processes in material development by utilizing data science and materials informatics (hereafter referred to as MI), moving away from the traditional trial-and-error approach based on experiments. These initiatives have been surpassing the limitations of human-designed patterns and the number of feasible experiments, and examples of such endeavors are starting to be reported even in the field of battery materials[1]. In this study, we challenged a MI technology combined with first-principles calculation and database technology to widely search for suitable compositions of Li-rich cathode materials at high speed.
Li1.4(Mn0.667Ni0.333)1-x

Mx
O2.35F0.05 (hereafter referred to as LMNOF, M: a dopant element, 0 ≤ x ≤ 0.01) was fabricated by a conventional method. The cathode was fabricated by mixing powder of the active material, acetylene black as a conducting powder and PTFE as a binder at the ratio of 7:2:1 by weight. The electrochemical performance was investigated using 2032 coin type half cells. The models for predicting battery performance were constructed by machine learning techniques. Composition formula information, synthesis conditions, and cell weight were used as candidate explanatory variables. XenonPy[2] was used for elemental-derived information. Principal component analysis was performed on the candidate explanatory variables, and only the variables determined to be important were used to construct the predictive model. The number of candidate M was narrowed down from approximately 100 to 30 based on the values of the enthalpy change between LMNOF with and without M. The enthalpy was calculated to determine the dopant elements for LMNOF by first-principles calculation. Specific M elements will be reported at the time of the presentation. The oxygen desorption energies of LMNOF with the 30 extracted elements were calculated to evaluate the stability of the LMNOF structure against the reaction in the fully charged state. Ten elements with negative values of the energy were selected as the dopant ones (Ms) for experimental demonstration of this study and the rest were used to predict the performance by using the database of the Materials Project[3]. The battery performance of M-doped LMNOF were predicted by the constructed model based on machine learning techniques with experimental data. This model indicated that the performances of LMNOF with a certain element M1 or M2 tended to be higher than ones with another M dopant elements at the stage where there had been no experimental data for M1 and M2. When the experiment was conducted for M1 and M2, the obtained data was turned out to be close to the predicted values and higher than ones with another M. These experimental demonstrations revealed that the constructed model at the initial stage in this study was suitable to predict the reasonable values for the composition without adequate experimental data. The loop of accumulating the experimental data and constructing the prediction model have been continuously carried out to improve the accuracy of predictions. As the number of experimental trials increased, the performance was gradually improved and the composition including M1 alone, M2 alone, M1 and other multiple Ms together, or M2 and other multiple Ms together, showed relatively higher performance, which, we considered, surpassed the maximum values obtained by the conventional trial-and-error experimental method. As a result, our new MI technology was demonstrated as an effective method to search for an unknown and unexplored composition with high performance due to the prediction accuracy of machine learning. In future, we are going to further improve prediction accuracy to find multiple Ms for even higher performance.
References
[1] Y. Mo, S. P. Ong, and G. Ceder, Chem. Mater., 24, 15-17 (2012).
[2] XenonPy: https://github.com/yoshida-lab/XenonPy
[3] A. Jain, S. P. Ong, G. Hautier, W. Chen, W. D. Richards, S. Dacek, S. Cholia, D. Gunter, D. Skinner, G. Ceder, and K. A. Persson, APL Mater., 1, 011002(2013).