Machine learning for data-driven design of high-safety lithium metal anode

Zhang，Qi1; Dong，Junlin2; Zhou，Chuan3; Zhang，Dantong1; Yuan，Shuguang2; Kramer，Denis4,5; Xue，Dongfeng1; Peng，Chao1

2024-03-15

10.1016/j.xpro.2023.102834

STAR Protocols   影响因子和分区

2666-1667

Here, we present a protocol for developing an inorganic-organic hybrid interphase layer using the self-assembled monolayers technique to enhance the surface of the lithium metal anode. We describe steps for extracting organic molecules from open-sourced databases and calculating their microscopic properties. We then detail procedures for developing a machine learning model for predicting the ionic diffusion barrier and preparing the inputs for prediction. This protocol enables a cost-effective workflow to identify promising self-assembled monolayers with exceptional performance. For complete details on the use and execution of this protocol, please refer to Zhang et al. (2023).

Chemistry Energy High Throughput Screening Material sciences

ESCI

英语

其他

2-s2.0-85182245659

Scopus

1.Multiscale Crystal Materials Research Center,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen,518055,China
2.University of Chinese Academy of Sciences,Beijing,100049,China
3.Department of Materials Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
4.Engineering Sciences,University of Southampton,Southampton,SO17 1BJ,United Kingdom
5.Helmut-Schmidt-University,University of the Armed Forces,Hamburg,22043,Germany

Zhang，Qi,Dong，Junlin,Zhou，Chuan,et al. Machine learning for data-driven design of high-safety lithium metal anode[J]. STAR Protocols,2024,5(1).

Zhang，Qi.,Dong，Junlin.,Zhou，Chuan.,Zhang，Dantong.,Yuan，Shuguang.,...&Peng，Chao.(2024).Machine learning for data-driven design of high-safety lithium metal anode.STAR Protocols,5(1).

Zhang，Qi,et al."Machine learning for data-driven design of high-safety lithium metal anode".STAR Protocols 5.1(2024).