Data‐Driven Battery Characterization and Prognosis: Recent Progress, Challenges, and Prospects

Ji, Shanling; Zhu, Jianxiong; Yang, Yaxin; dos Reis, Gonçalo; Zhang, Zhisheng

doi:10.1002/smtd.202301021

Cited by 2 publications

(1 citation statement)

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“…The core thought of ML is to identify the complex relationship between multivariable features and the associated-dependent variable. Based on ML’s core principle, it has already become increasingly prevalent in the material science discipline, notably in battery technologies, predictions of crystal stability, and TEs. − …”

Section: Introductionmentioning

confidence: 99%

Interpretable Machine Learning Model on Thermal Conductivity Using Publicly Available Datasets and Our Internal Lab Dataset

Barua,

Hall,

Cheng

et al. 2024

Chem. Mater.

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Machine learning (ML), a subdiscipline of artificial intelligence studies, has gained importance in predicting or suggesting efficient thermoelectric materials. Previous ML studies have used different literature sources or density functional theory calculations as input. In this work, we develop a ML pipeline trained with multivariable inputs on a massive public dataset of ∼200,000 data utilizing a high-performance computing cluster to predict the thermal conductivity (κ) using four test sets: three publicly available datasets and a dataset built using previously published data from our own group. By taking advantage of this massive dataset, our model presents an opportunity to further expand the understanding of the selection of features with various thermoelectric materials. Among the several supervised ML models implemented, the eXtreme Gradient Boosting algorithm (XGBoost) turned out to be the best method during the 5-fold cross-validation method, with their averaged evaluation coefficients of R 2 = 0.96, root mean squared error (RMSE) = 0.38 W m −1 K −1 , and mean absolute error (MAE) = 0.23 W m −1 K −1 . Additionally, with the aid of feature selection and importance analysis, useful chemical features were chosen that ultimately led to reasonably good accuracy in the series of test sets measured as per the evaluation coefficients of R 2 , RMSE, and MAE, with values ranging from 0.72 to 0.89, 0.52 to 1.08, and 0.40 to 0.66 W m −1 K −1 , respectively. Checking the worst outliers led to the discovery of some errors in the literature. Postmodel prediction, the SHapley Additive exPlanations (SHAP) algorithm was implemented on the XGBoost model to analyze the features that were the key drivers for the model's decisions. Overall, the developed interpretable methodology produces the prediction of κ of a large variety of materials through the influence of chemical and physical property features. The conclusions drawn apply to the research and applications of thermoelectric and heat insulation materials.

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Section: Introductionmentioning

confidence: 99%