In the development of battery science,machine learning(ML)has been widely employed to predict material properties,monitor morphological variations,learn the underlying physical rules and simplify the material-discover...In the development of battery science,machine learning(ML)has been widely employed to predict material properties,monitor morphological variations,learn the underlying physical rules and simplify the material-discovery processes.However,the widespread adoption of ML in battery research has encountered limitations,such as the incomplete and unfocused databases,the low model accuracy and the difficulty in realizing experimental validation.It is significant to construct the dataset containing specific-domain knowledge with suitable ML models for battery research from the applicationoriented perspective.We outline five key challenges in the field and highlight potential research directions that can unlock the full potential of ML in advancing battery technologies.展开更多
基金the support of the National Natural Science Foundation of China(92372126,52373203,Excellent Young Scientists Fund Program)the AI for Science Foundation of Fudan University(FudanX24AI014).
文摘In the development of battery science,machine learning(ML)has been widely employed to predict material properties,monitor morphological variations,learn the underlying physical rules and simplify the material-discovery processes.However,the widespread adoption of ML in battery research has encountered limitations,such as the incomplete and unfocused databases,the low model accuracy and the difficulty in realizing experimental validation.It is significant to construct the dataset containing specific-domain knowledge with suitable ML models for battery research from the applicationoriented perspective.We outline five key challenges in the field and highlight potential research directions that can unlock the full potential of ML in advancing battery technologies.
