摘要
机器学习已成为新材料研发的重要变革性手段,但材料数据样本量少、噪音高等特点为数据驱动的研发模式带来巨大挑战。本工作将无监督学习应用于挖掘高介电常数的钙钛矿材料。针对标签数据少的问题,通过聚类学习的方法不断优化迭代来缩小搜索空间,最终筛选出了BaHfO_(3)和BiFeO_(3)等20种具有高介电常数潜力的钙钛矿材料,并通过降维分析等手段从元素种类、晶体结构和容忍因子等方面展开规律分析,挖掘钙钛矿材料结构与介电常数之间的关联。该方法为解决材料性能数据标签的缺失提供了一种思路,可应用于筛选和挖掘其他新型功能材料。
Machine learning has become an important transformative method to explore novel materials,but the small sample size and high noise of material data bring a great challenge to data-driven research and development.To address the challenge,unsupervised learning was applied to discover perovskite materials with a high dielectric constant.Twenty perovskite materials with a high dielectric constant (i.e.,BaHfO_3 and BiFeO_3) were screened out via iterative clustering.We performed dimensionality reduction analysis and descriptors analysis including elements,crystal structure and tolerance factors to find the underlying trend and the relationship between ABO_3 structure and dielectric constant.This method can provide an idea for solving the lack of material data labels,which can be also applied to screen other novel functional materials.
作者
刘润林
李长姣
王建
刘韩星
沈忠慧
LIU Runlin;LI Changjiao;WANG Jian;LIU Hanxing;SHEN Zhonghui(International School of Materials Science and Engineering,Wuhan University of Technology,Wuhan 430070,China;School of Materials Science and Engineering,Wuhan University of Technology,Wuhan 430070,China)
出处
《硅酸盐学报》
EI
CAS
CSCD
北大核心
2023年第2期367-372,共6页
Journal of The Chinese Ceramic Society
基金
国家自然科学基金青年科学基金项目(52002300)
国家自然科学基金重大研究计划培育项目(92066103)
中国科协青年人才托举工程(2019QNRC001)。
关键词
介电常数
机器学习
钙钛矿材料
电介质材料
dielectric constant
machine learning
perovskite materials
dielectrics