The importance of precise and suitable descriptors in data-driven approach to boost development of lithium batteries: A perspective

Abstract

Conventional approaches for developing new materials may no longer be adequate to meet the urgent needs of humanity's energy transition. The emergence of machine learning (ML) and artificial intelligence (AI) has led materials scientists to recognize the potential of using AI/ML to accelerate the creation of new battery materials. Although fixed material properties have been extensively studied as descriptors to establish the link between AI and materials chemistry, they often lack versatility and accuracy due to a lack of understanding of the underlying mechanisms of AI/ML. Therefore, materials scientists need to have a comprehensive understanding of the operational mechanisms and learning logic of AI/ML to design more accurate descriptors. This paper provides a review of previous research studies conducted on AI, ML, and descriptors, which have been used to address challenges at various levels, ranging from materials development to battery performance prediction. Additionally, it introduces the basics of AI and ML to assist materials and battery developers in comprehending their operational mechanisms. The paper demonstrates the significance of precise and suitable ML descriptors in the creation of new battery materials. It does so by providing examples, summarizing current descriptors and ML algorithms, and examining the potential implications of future AI advancements for the sustainable energy industry.