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A dynamic database of solid-state electrolyte(DDSE)picturing all-solid-state batteries
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作者 Fangling Yang Egon Campos dos Santos +5 位作者 Xue Jia Ryuhei Sato Kazuaki Kisu yusuke hashimoto Shin-ichi Orimo Hao Li 《Nano Materials Science》 EI CAS CSCD 2024年第2期256-262,共7页
All-solid-state batteries(ASSBs)are a class of safer and higher-energy-density materials compared to conventional devices,from which solid-state electrolytes(SSEs)are their essential components.To date,investigations ... All-solid-state batteries(ASSBs)are a class of safer and higher-energy-density materials compared to conventional devices,from which solid-state electrolytes(SSEs)are their essential components.To date,investigations to search for high ion-conducting solid-state electrolytes have attracted broad concern.However,obtaining SSEs with high ionic conductivity is challenging due to the complex structural information and the less-explored structure-performance relationship.To provide a solution to these challenges,developing a database containing typical SSEs from available experimental reports would be a new avenue to understand the structureperformance relationships and find out new design guidelines for reasonable SSEs.Herein,a dynamic experimental database containing>600 materials was developed in a wide range of temperatures(132.40–1261.60 K),including mono-and divalent cations(e.g.,Li^(+),Na^(+),K^(+),Ag^(+),Ca^(2+),Mg^(2+),and Zn^(2+))and various types of anions(e.g.,halide,hydride,sulfide,and oxide).Data-mining was conducted to explore the relationships among different variates(e.g.,transport ion,composition,activation energy,and conductivity).Overall,we expect that this database can provide essential guidelines for the design and development of high-performance SSEs in ASSB applications.This database is dynamically updated,which can be accessed via our open-source online system. 展开更多
关键词 Solid-state electrolyte(SSE) All-solid-state battery(ASSB) Ionic conductivity Dynamic database Machine learning
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应对热电材料人工智能领域的大数据挑战 被引量:1
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作者 贾雪 Alex Aziz +1 位作者 yusuke hashimoto 李昊 《Science China Materials》 SCIE EI CAS CSCD 2024年第4期1173-1182,共10页
人工智能的发展正在改变材料科学领域.然而,大规模材料数据集中存在错误数据以及利用机器学习预测与温度相关的性质时出现过拟合等挑战.本文以热电材料为例,首先采取一系列合理的方法删除问题数据,从Starrydata2数据库中获得包括7295种... 人工智能的发展正在改变材料科学领域.然而,大规模材料数据集中存在错误数据以及利用机器学习预测与温度相关的性质时出现过拟合等挑战.本文以热电材料为例,首先采取一系列合理的方法删除问题数据,从Starrydata2数据库中获得包括7295种成分在不同温度下的92,291个数据.然后,提出了一种基于成分的交叉验证方法避免过拟合.进而,使用梯度提升决策树方法构建了机器学习模型,并获得了显著的R2.最后,使用该模型对Materials Project数据库中的材料进行评估,Ge2Te5As2和Ge3(Te3As)2表现出较高的zT值.理论计算得到n型和p型Ge2Te5As2的最大zT值为1.98和2.12,n型和p型Ge3(Te3As)2的最大zT值为0.58和0.74,表明它们是有潜力的热电材料.本工作提出了一个处理和克服材料科学中的人工智能大数据挑战的示例. 展开更多
关键词 人工智能 大数据 机器学习 过拟合 热电材料 材料数据 数据库 温度相关
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