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基于材料基因工程方法的固态锂离子导体材料快速搜索研究

税烺 付建辉

税烺, 付建辉. 基于材料基因工程方法的固态锂离子导体材料快速搜索研究[J]. 钢铁钒钛, 2022, 43(6): 193-200. doi: 10.7513/j.issn.1004-7638.2022.06.029
引用本文: 税烺, 付建辉. 基于材料基因工程方法的固态锂离子导体材料快速搜索研究[J]. 钢铁钒钛, 2022, 43(6): 193-200. doi: 10.7513/j.issn.1004-7638.2022.06.029
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
Citation: 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

基于材料基因工程方法的固态锂离子导体材料快速搜索研究

doi: 10.7513/j.issn.1004-7638.2022.06.029
详细信息
    作者简介:

    税烺,1987年出生,男,博士,高级工程师,通讯作者,主要从事材料基因工程、高温耐蚀合金研究,E-mail:ustb1234@126.com

    通讯作者:

    税烺,1987年出生,男,博士,高级工程师,通讯作者,主要从事材料基因工程、高温耐蚀合金研究,E-mail:ustb1234@126.com

  • 中图分类号: TB33

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

  • 摘要: 运用决策树算法和随机森林算法来构建针对固态超离子导体的筛选模型。基于从文献收集的数据集和20个基于材料晶格常数的参数,建立了两种决策树模型、一种随机森林模型和一种作为对比的逻辑回归模型。通过对比,随机森林模型展示出较低的算法复杂度和较好的泛化能力。这些训练好的模型随后被用于筛选Material Project数据库中的含锂的化合物。随机森林模型的筛选结果将候选材料总数降低了87.76%,其中包含有数种已知的超离子导体材料,因而展现出了该模型的可靠性和高效性。所使用的模型建立方法可以显著减少搜寻理想物理属性的材料所需要的时间,从而加速了新材料的研发过程。
  • 图  1  二维特征空间划分示意

    Figure  1.  Schematic diagram of a 2D feature space division

    图  2  决策树1:采用整个数据集训练,未剪枝

    Figure  2.  Decision tree 1: trained by the entire data set with no pruning

    图  3  决策树2:使用训练集数据训练并使用验证集数据剪枝

    Figure  3.  Decision tree 2: trained by samples in train set and pruned by samples in validation set

    图  4  一个包含M棵决策树的随机森林模型工作过程示意

    Figure  4.  Schematic working flow of a random forest with M trees

    图  5  随机森林的预测精度随决策树的个数的变化过程

    Figure  5.  Precision rate of random forest vs. number of trees in the forest

    图  6  误分类率随特征数的变化

    Figure  6.  Miss classification rate vs. number of features

    表  1  随机森林模型与逻辑回归模筛选结果对比

    Table  1.   Screening results comparison of the random forest model and logistic regression model

    Random forest, 200 treesLogistic regression
    Li3As1H36Se4N12Li3As1H36Se4N12
    Cs3Li3H12N6Cs3Li3H12N6
    Rb12Li2Nd22Se24Cl32O72Rb12Li2Nd22Se24Cl32O72
    Li1Ca4B3N6Li1Ca4B3N6
    Cs4Li2In2Cl12Cs4Li2In2Cl12
    Cs2Li1Al3F12Cs2Li1Al3F12
    Ba4Li1Sb3O12Ba4Li1Sb3O12
    K4Li2Al2F12K4Li2Al2F12
    Rb4Li2Ga2F12Rb4Li2Ga2F12
    Sr4Li1B3N6Sr4Li1B3N6
    Li9Er3Cl18Li9Er3Cl18
    Cs4Li6Ga2O8Cs4Li6Ga2O8
    Rb12Li2Pr22Se24Cl32O72Rb12Li2Pr22Se24Cl32O72
    Li2H6O4Na8Li12Ga4O16
    Li12Gd4B8O24K2Li2Si4O10
    Li2H12Br2O14Sr8Li4C4Br12N8
    Li1Sb1F6Li1Er1Se2
    Li1As1F6Li4In4I16
    K8Li32Al8O32Li1Dy1Se2
    Cs16Li8Si24O60Li1Ho1Se2
    Li2Si1Sn1S4Rb2Li2S2
    Li6U1O6K20Li4Ge8O28
    Li1P1F6Li40Al8O32
    Li6Bi2O8Li1Er1S2
    Li4Er4O8Li1Tb1Se2
    Li2Tm2O4Li40Ga8O32
    K1Li6Bi1O6Li18In6Cl36
    Li2Mg2B6H36N4K8Li4B4P8O32
    Li4Tm4Si4O16Sr4Li16Ca4Si8O32
    Rb4Li4Si2O8Li8Te4O12
    Li2In2O4Li4Ga4Br16
    Li4Ca12Si8N20Li4Ga4I16
    Sr2Li2Pr2Te2O12K4Li2B2O6
    Li4Ho4O8Li2Sn4P10O30
    K2Li6Pb2O8Li4Ca36Mg4P28O112
    Li1H1F2Na12Li12In8F48
    Li2Ge1Pb1S4Li6Er2Br12
    Li4Ca2Mg1Si2N6Li2La4Sb2O12
    Li2Sm2S4Li1Ho1S2
    Li2Ca1Si1O4Na12Li12Al8F48
    Li2Sm2Se4Li1Dy1S2
    Li2Ca1Ge1O4Ba4Li4B4S12
    下载: 导出CSV
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出版历程
  • 收稿日期:  2022-04-18
  • 刊出日期:  2023-01-13

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