Reshaping the material research paradigm of electrochemical energy storage and conversion by machine learning
Hao Yang,
Zhengqiu He,
Mengdi Zhang,
Xiaojie Tan,
Kang Sun,
Haiyan Liu,
Ning Wang,
Lu Guan,
Chongze Wang,
Yi Wan,
Wanli Wang,
Han Hu,
Mingbo Wu
Affiliations
Hao Yang
State Key Laboratory of Heavy Oil Processing, Institute of New Energy, College of Chemistry and Chemical Engineering China University of Petroleum (East China) Qingdao China
Zhengqiu He
State Key Laboratory of Heavy Oil Processing, Institute of New Energy, College of Chemistry and Chemical Engineering China University of Petroleum (East China) Qingdao China
Mengdi Zhang
State Key Laboratory of Heavy Oil Processing, Institute of New Energy, College of Chemistry and Chemical Engineering China University of Petroleum (East China) Qingdao China
Xiaojie Tan
State Key Laboratory of Heavy Oil Processing, Institute of New Energy, College of Chemistry and Chemical Engineering China University of Petroleum (East China) Qingdao China
Kang Sun
Institute of Chemical Industry of Forest Products Chinese Academy of Forestry Nanjing China
Haiyan Liu
National Engineering Research Center of Coal Gasification and Coal‐Based Advanced Materials Shandong Energy Group CO., LTD Jinan China
Ning Wang
State Key Laboratory of Heavy Oil Processing, Institute of New Energy, College of Chemistry and Chemical Engineering China University of Petroleum (East China) Qingdao China
Lu Guan
State Key Laboratory of Heavy Oil Processing, Institute of New Energy, College of Chemistry and Chemical Engineering China University of Petroleum (East China) Qingdao China
Chongze Wang
State Key Laboratory of Heavy Oil Processing, Institute of New Energy, College of Chemistry and Chemical Engineering China University of Petroleum (East China) Qingdao China
Yi Wan
State Key Laboratory of Heavy Oil Processing, Institute of New Energy, College of Chemistry and Chemical Engineering China University of Petroleum (East China) Qingdao China
Wanli Wang
State Key Laboratory of Heavy Oil Processing, Institute of New Energy, College of Chemistry and Chemical Engineering China University of Petroleum (East China) Qingdao China
Han Hu
State Key Laboratory of Heavy Oil Processing, Institute of New Energy, College of Chemistry and Chemical Engineering China University of Petroleum (East China) Qingdao China
Mingbo Wu
State Key Laboratory of Heavy Oil Processing, Institute of New Energy, College of Chemistry and Chemical Engineering China University of Petroleum (East China) Qingdao China
Abstract For a “Carbon Neutrality” society, electrochemical energy storage and conversion (EESC) devices are urgently needed to facilitate the smooth utilization of renewable and sustainable energy where the electrode materials and catalysts play a decisive role. However, the efficiency of the current trial‐and‐error research paradigm largely lags behind the imminent demands of EESC requiring increasingly improved performance. The emerged machine learning (ML), a subfield of artificial intelligence, is capable of evaluating and analyzing big data for hidden rules. In this regard, the relationships between the structure and performance of the key materials can be more efficiently revealed, which fundamentally revolutionizes the material research manner of the current EESC devices. In this review, the typical ML algorithms utilized in EESC development are first introduced. Then, focused attention has been paid to multiple aspects of applying ML to reshape the materials research for EESC. In addition to highlighting the emerging prospect, the challenges which are still hindering the further development of this emerging field are also discussed.
