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  

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

作　　者：税烺 付建辉 Shui Lang;Fu Jianhui(Chengdu Institute of Advanced Metallic Material Technology and Industry Co.,Ltd.,Chengdu 610303,Sichuan,China;State Key Laboratory of Metal Material for Marine Equipment and Application,Anshan 114009,Liaoning,China)

机构地区：[1]成都先进金属材料产业技术研究院股份有限公司特钢技术研究所,四川成都610303 [2]海洋装备用金属材料及其应用国家重点实验室,辽宁鞍山114009

出　　处：《钢铁钒钛》2022年第6期193-200,共8页Iron Steel Vanadium Titanium

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。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.

关 键 词：固态超锂离子导体 材料基因工程 机器学习 随机森林 高通量筛选 

分 类 号：TB33[一般工业技术—材料科学与工程]