Accelerated search for solid lithium-ion conductor materials based on material genome engineering

Shui Lang; Fu Jianhui

doi:10.7513/j.issn.1004-7638.2022.06.029

Volume 43 Issue 6

Jan. 2023

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Shui Lang, Fu Jianhui. Accelerated search for solid lithium-ion conductor materials based on material genome engineering[J]. IRON STEEL VANADIUM TITANIUM, 2022, 43(6): 193-200. doi: 10.7513/j.issn.1004-7638.2022.06.029

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Shui Lang, Fu Jianhui. Accelerated search for solid lithium-ion conductor materials based on material genome engineering[J]. IRON STEEL VANADIUM TITANIUM, 2022, 43(6): 193-200. doi: 10.7513/j.issn.1004-7638.2022.06.029

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Accelerated search for solid lithium-ion conductor materials based on material genome engineering

doi: 10.7513/j.issn.1004-7638.2022.06.029

Shui Lang^{1, 2
,
,},
Fu Jianhui^{1, 2}

1.
Chengdu Institute of Advanced Metallic Material Technology and Industry Co., Ltd., Chengdu 610303, Sichuan, China
2.
State Key Laboratory of Metal Material for Marine Equipment and Application, Anshan 114009, Liaoning, China

Received Date: 2022-04-18
Publish Date: 2023-01-13

Abstract

Abstract

We here present a new approach of model construction to search for solid superionic materials in database by using decision tree and random forest algorithms. Based on a data set collected from literature and 20 features computed from lattice parameters, we constructed two decision tree models and a random forest model, as well as a logistic regression model for contrast. In comparison, the random forest model shows low algorithm complexity and better generalization ability. The well-trained models are then used to screen lithium-containing compounds in the material project database. Screening results of the random forest model reduce the candidate materials by 87.76% and consist of several known superionic materials, which exhibits efficiency and effectiveness of the model. The methodology of model building introduced here can remarkably reduce the searching range of materials with desired properties and thus accelerates the development of new materials.
- solid lithium-ion conductor,
- material genome engineering,
- machine learning,
- random forest,
- high throughput screening

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References(22)

References

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