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Progress in Chemistry 2023, Vol. 35 Issue (4): 577-592 DOI: 10.7536/PC220937 Previous Articles   Next Articles

• Review •

Application of Machine Learning in the Design of Cathode Materials and Electrolytes for High-Performance Lithium Batteries

Zhendong Liu1, Jiajie Pan1, Quanbing Liu1,2()   

  1. 1. Guangzhou Key Laboratory of Clean Transportation Energy Chemistry, Guangdong Provincial Key Laboratory of Plant Resources Biorefinery, School of Chemical Engineering and Light Industry, Guangdong University of Technology,Guangzhou 510006, China
    2. Jieyang Branch of Chemistry and Chemical Engineering Guangdong Laboratory,Jieyang 515200, China
  • Received: Revised: Online: Published:
  • Contact: *e-mail: liuqb@gdut.edu.cn (Quanbing Liu)
  • Supported by:
    Key-Area Research and Development Program of Guangdong Province(2020B090919005); National Natural Science Foundation of China(22179025); National Natural Science Foundation of China(21905056); National Natural Science Foundation of China(21975056)
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The rapid application of big data and artificial intelligence, and the deep intersection of machine learning (ML) and chemistry disciplines have inspired more promising development approaches for the integration of ML technology with battery materials, especially in the material design of battery, performance prediction, structure optimization, and so on. The application of ML can effectively accelerate the selection process of battery materials and predict the performance of lithium batteries (LBs), consequently driving the development of LBs. This review briefly introduces the basic idea of ML and several important ML algorithms in the field of LBs, then the error performance and analysis of the traditional simulation calculation method and ML method are discussed, thereby increasing understanding of ML methods by LBs experts. Secondly, the application of ML in the practical development of battery materials, including cathode materials, electrolytes, multi-scale simulation of materials and high-throughput experiments (HTE), is emphatically introduced to draw out the ideas and means of applying ML methods in the field of batteries. Finally, the recent works of ML in lithium batteries are summarized and their application prospects are foreseen. It is hoped that this review will shed light on the application of ML in the development of LBs and promote the development of advanced LBs.

Key words: lithium battery, machine learning, material screening, material design, performance prediction
Fig.1 (a) Construction of the crystal graph. Crystals are converted to graphs with nodes representing atoms in the unit cell and edges representing atom connections. (b) Structure of the convolutional neural network on top of the crystal graph. with Each node representing the local environment of each atom. Copyright 2018, PRL
Fig.2 Algorithm classification and common algorithms of ML in the field of batteries
Fig.3 The overall working principle of the ML method for supervised/unsupervised and classification/regression methods[25]. Copyright 2021, ACS
Table 1 Common machine learning algorithm advantages and its disadvantages
Method Category Features
Artificial neural network Regression Requires a large amount of data, relatively strong self-learning and fault tolerance, can analyze complex linear relationships, but the interpretability is weak
Linear regression Regression First make the assumption that the data set requires linear consistency, faster modeling, and good interpretability
Ridge Return Regression Can handle non-linear data, but the prediction efficiency decreases when the data volume is particularly large
Polynomial regression Regression Rapid modeling, effective for small data volumes and simple relationships, difficult to accurately represent high-dimensional complex data
Support vector classification Classification Also known as the maximum margin classifier, it is an important classification model that is mostly applicable to binary data
K-Nearest Neighbor Classification Suitable for multi-classification models, but the computational effort is larger compared to other algorithms, and the data set samples are more demanding
Decision Trees Classification Can handle data with missing attributes, good interpretability, but prone to overfitting
Random Forest Classification Not only does it have the advantages of decision trees, but it also prevents overfitting
K-Means clustering Clustering It is a classical clustering algorithm with simple and fast features, but the algorithm requires high quality for the initial data set
Hierarchical Cluster Analysis Clustering By building a hierarchy of clusters, the whole clustering process can be done at once, but it is computationally intensive
Fig.4 Schematics of some of the most common ML algorithms. (a) Neural networks; (b) K-nearest neighbors; (c) support vector machines; (d) linear regression
Fig.5 Combining the correlation plots of energy with DFT and ML, and the histogram of error distribution, (a), (c), (e) are based on ab initio algorithm, (b), (d), (f) are based on migration learning algorithm[59]. Copyright 2021, ESM
Fig.6 Training data set. The x-axis is a distance-weighted Steinhart order parameter (OP), the y-axis is the density of each configuration. The colored points correspond to different x of (LiCl)1-x(ZrCl4)x[70]. Copyright 2022, Nano Lett
Fig.7 Experimental flowchart. Data sets are obtained by preparing different slurries, and then samples with different properties are classified with the help of different ML algorithms, and finally an ideal solid electrolyte film is obtained[71]. Copyright 2021, ACS
Fig.8 Workflow for constructing a force field model using machine learning[91]. (a) Schematic of sampling from molecular dynamics trajectories; (b) schematic of the resulting dataset in constructing a machine learning force field; (c) schematic of the application;(d) distribution of interatomic distances sampled by molecular dynamics. Copyright 2020, Rev. Phys. Chem
Fig.9 Otto system components[97]. (a) Single line flow diagram of test system; (b) graphical representation of system components. Copyright 2019, ECS
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