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| 1 | Review on modeling of the anode solid electrolyte interphase (SEI) for lithium-ion batteries显示文摘A passivation layer called the solid electrolyte interphase(SEI)is formed on electrode surfaces from decomposition products of electrolytes.The SEI allows Li+transport and blocks electrons in order to prevent further electrolyte decomposition and ensure continued electrochemical reactions.The formation and growth mechanism of the nanometer thick SEI films are yet to be completely understood owing to their complex structure and lack of reliable in situ experimental techniques.Significant advances in computational methods have made it possible to predictively model the fundamentals of SEI.This review aims to give an overview of state-of-the-art modeling progress in the investigation of SEI films on the anodes,ranging from electronic structure calculations to mesoscale modeling,covering the thermodynamics and kinetics of electrolyte reduction reactions,SEI formation,modification through electrolyte design,correlation of SEI properties with battery performance,and the artificial SEI design.Multiscale simulations have been summarized and compared with each other as well as with experiments.Computational details of the fundamental properties of SEI,such as electron tunneling,Li-ion transport,chemical/mechanical stability of the bulk SEI and electrode/(SEI/)electrolyte interfaces have been discussed.This review shows the potential of computational approaches in the deconvolution of SEI properties and design of artificial SEI.We believe that computational modeling can be integrated with experiments to complement each other and lead to a better understanding of the complex SEI for the development of a highly efficient battery in the future. | Aiping Wang Sanket Kadam Hong Li Siqi Shi Yue Qi | 2018 | npj Computational Materials2018，，1： | 20 |
| 2 | Data mining-aided materials discovery and optimization显示文摘Recent developments in data mining-aided materials discovery and optimization are reviewed in this paper,and an introduction to the materials data mining(MDM)process is provided using case studies.Both qualitative and quantitative methods in machine learning can be adopted in the MDM process to accomplish different tasks in materials discovery,design,and optimization.State-of-the-art techniques in data mining-aided materials discovery and optimization are demonstrated by reviewing the controllable synthesis of dendritic Co_(3)O_(4) superstructures,materials design of layered double hydroxide,battery materials discovery,and thermoelectric materials design.The results of the case studies indicate that MDM is a powerful approach for use in materials discovery and innovation,and will play an important role in the development of the Materials Genome Initiative and Materials Informatics. | Wencong Lu Ruijuan Xiao Jiong Yang Hong Li Wenqing Zhang | 2017 | Journal of Materiomics2017，3，3： | 11 |
| 3 | 基于材料基因工程的锂离子电池材料数据生成及数据挖掘平台显示文摘利用材料基因工程方法探索锂离子电池新材料是目前国际上重要的技术手段.通过高通量计算手段计算生成大量锂离子电池材料的基本物理化学性质数据,利用数据挖掘技术,总结材料物理化学性质与材料的组分、组织结构等的构效关系,进而探索发现新型锂离子电池材料和改性现有材料.利用材料基因工程基本思想,设计了利用无机材料晶体结构数据库中的结构数据为源数据,通过基于Web服务器来产生第一性原理计算软件VASP的输入文件信息,并通过Web网传输到并行计算中心实现材料性能计算,生成的材料性质数据再返回到Web服务器.通过对材料结构和性质数据进行挖掘,总结锂离子电池材料的构效关系,为锂离子电池材料设计提供技术支持. | 肖建茂 汪浩 欧阳楚英 | 2015 | 江西师范大学学报(自然科学版)2015，39，1： | 3 |
| 4 | First-Principles Study of Lithium and Sodium Atoms Intercalation in Fluorinated Graphite显示文摘The structure evolution of fluorinated graphite(CFx) upon the Li/Na intercalation has been studied by firstprinciples calculations. The Li/Na adsorption on single CF layer and intercalated into bulk CF have been calculated. The better cycling performance of Na intercalation into the CF cathode, comparing to that of Li intercalation, is attributed to the different strength and characteristics of the Li-F and Na-F interactions. The interactions between Li and F are stronger and more localized than those between Na and F. The strong and localized Coulomb attraction between Li and F atoms breaks the C—F bonds and pulls the F atoms away, and graphene sheets are formed upon Li intercalation. | Fengya Rao Zhiqiang Wang Bo Xu Liquan Chen Chuying Ouyang | 2015 | Engineering2015，1，2： | 2 |
| 5 | 数据驱动的钢铁耐磨材料性能预测研究综述显示文摘数据驱动方法利用机器学习算法挖掘数据中隐藏的规则,是一种符合“第四范式”的研究方法。该研究方法的开展基于大量材料基础数据。通过对比国内外材料基础数据平台,分析利用现有数据平台已开展的研究,指出钢铁耐磨材料基础数据存在数据匮乏和缺乏统一采集标准两个问题。针对此,介绍符合材料基因组计划的数据采集标准,并给出钢铁耐磨材料专用数据平台的框架以及数据来源。分析钢铁耐磨材料性能的影响因素,讨论各种特征选择技术的特点。回顾在材料科学研究中成功应用的几种机器学习算法,分析每种算法的应用场景,讨论它们的优缺点,并对算法性能进行了比较。最后总结一些建议为特征提取和机器学习算法选择提供指导,并指出数据驱动方法在性能预测、发现新材料和自动化自主试验等方面具有良好的应用前景。 | 刘源 魏世忠 | 2022 | 机械工程学报2022，58，10： | 1 |
| 6 | Strain engineering of ion migration in LiCoO_(2)显示文摘Strain engineering is a powerful approach for tuning various properties of functional materials. The influences of lattice strain on the Li-ion migration energy barrier of lithium-ions in layered LiCoO_(2) have been systemically studied using lattice dynamics simulations, analytical function and neural network method. We have identified two Li-ion migration paths, oxygen dumbbell hop (ODH), and tetrahedral site hop (TSH) with different concentrations of local defects. We found that Li-ion migration energy barriers increased with the increase of pressure for both ODH and TSH cases, while decreased significantly with applied tensile uniaxial c-axis strain for ODH and TSH cases or compressive in-plane strain for TSH case. Our work provides the complete strain-map for enhancing the diffusivity of Li-ion in LiCoO_(2), and therefore, indicates a new way to achieve better rate performance through strain engineering. | Jia-Jing Li Yang Dai Jin-Cheng Zheng | 2022 | Frontiers of physics2022，17，1： | 1 |
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