Data-driven discovery and intelligent design of artificial hybrid interphase layer for stabilizing lithium-metal anode

Zhang，Qi1; Zhou，Chuan2; Zhang，Dantong1; Kramer，Denis3; Peng，Chao1; Xue，Dongfeng1

2023-09-06

10.1016/j.matt.2023.06.010

Matter   影响因子和分区

2590-2393

2590-2385

Lithium metal is a promising anode material for high-energy-density batteries, but its application is hindered by safety concerns arising from dendrite growth. In this work, we propose a high-throughput workflow that combines quantum-mechanical simulations with machine learning to accurately predict self-assembled monolayers (SAMs) that can assemble an artificial inorganic-organic hybrid interphase layer on the Li-metal anode to enhance cycling stability and mitigate dendrite growth. The workflow comprises automatic data collection, first-principles simulations, and screening of candidate molecules using machine learning. We screened out 128 molecules from the PubChem database and identified the eight best candidates with low Li diffusion barriers and high mechanical stability. A structure-property relationship was established between the Li diffusion barrier and the structural characteristics of head, middle, and tail groups in the SAMs using simple quantum mechanical (QM) dipole and electrostatic potential descriptors. These results open new avenues for designing highly stable Li-metal anodes for practical use in Li-metal batteries.

high-throughput workflow inorganic-organic hybrid interphase layer Li dendrite Li-metal anode machine learning MAP5: Improvement self-assembled molecules

SCI ; EI

英语

其他

Natural Science Foundation of Guangdong Province[2022A1515010076];Chinese Academy of Sciences[2022VEA0011];Chinese Academy of Sciences[2022VEA0016];Chinese Academy of Sciences[2022VEA0017];National Natural Science Foundation of China[52203303];National Natural Science Foundation of China[52220105010];Shenzhen Institutes of Advanced Technology Innovation Program for Excellent Young Researchers[E2G017];National Natural Science Foundation of China[M-0755];

Materials Science

Materials Science, Multidisciplinary

WOS:001073883100001

CELL PRESS

20233614674770

Anodes ; Diffusion barriers ; Lithium ; Lithium batteries ; Machine learning ; Mechanical stability ; Quantum theory ; Self assembled monolayers

Lithium and Alloys:542.4 ; Alkali Metals:549.1 ; Primary Batteries:702.1.1 ; Electron Tubes:714.1 ; Artificial Intelligence:723.4 ; Atomic and Molecular Physics:931.3 ; Quantum Theory; Quantum Mechanics:931.4

2-s2.0-85169448919

Scopus

1.Multiscale Crystal Materials Research Center,Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences,Shenzhen,518055,China
2.Department of Materials Science and Engineering,Southern University of Science and Technology,Shenzhen,518055,China
3.Helmut-Schmidt-University,University of the Armed Forces,Hamburg,22043,Germany

Zhang，Qi,Zhou，Chuan,Zhang，Dantong,et al. Data-driven discovery and intelligent design of artificial hybrid interphase layer for stabilizing lithium-metal anode[J]. Matter,2023,6(9):2950-2962.

Zhang，Qi,Zhou，Chuan,Zhang，Dantong,Kramer，Denis,Peng，Chao,&Xue，Dongfeng.(2023).Data-driven discovery and intelligent design of artificial hybrid interphase layer for stabilizing lithium-metal anode.Matter,6(9),2950-2962.

Zhang，Qi,et al."Data-driven discovery and intelligent design of artificial hybrid interphase layer for stabilizing lithium-metal anode".Matter 6.9(2023):2950-2962.