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| 1 | 基于分层编码晶体结构(HECS)描述符的机器学习预测立方相锂-硫银锗矿电解质材料的激活能显示文摘合理设计高离子电导率和低活化能(Ea)的固态电解质(SSEs)对全固态电池至关重要.近年来,基于各种描述符的机器学习技术成功地预测了锂离子在SSEs中的传导特性.本文建立了一个通用的基于HECS描述符的机器学习预测无机SSEs材料Ea的框架,并且以立方相锂-硫银锗矿型SSEs材料作为模型体系进行实例研究,采用偏最小二乘方法(PLS)建立了高精度(训练集:R^(2),88.7%;RMSE,0.02 e V;测试集:R^(2),82.0%;RMSE,0.02 e V)预测E_(a)的模型.变量投影重要性(VIP)分析表明了全局及局域离子传导环境对E_(a)的联合作用,其中平均阴离子尺寸以及与阴离子位置无序密切相关的结构的改变对激活能值的贡献尤为突出,这一发现有助于进一步指导发现或设计新的无机固态电解质材料.同时,对该模型进行的知识提取表明,可以通过增大瓶颈尺寸、引发阴离子位置无序、激活离子协同迁移等优化和设计出具有高离子传导性能的新的无机固态电解质材料. | 赵倩 Maxim Avdeev 陈立泉 施思齐 | 2021 | Science Bulletin2021，66，14： | 5 |
| 2 | Understanding the Li diffusion mechanism and positive effect of current collector volume expansion in anode free batteries显示文摘In anode free batteries(AFBs), the current collector acts as anode simultaneously and has large volume expansion which is generally considered as a negative effect decreasing the structural stability of a battery. Moreover, despite many studies on the fast lithium diffusion in the current collector materials of AFB such as copper and aluminum, the involved Li diffusion mechanism in these materials remains poorly understood. Through first-principles calculation and stress-assisted diffusion equations, here we study the Li diffusion mechanism in several current collectors and related alloys and clarify the effect of volume expansion on Li diffusion respectively. It is suggested that due to the lower Li migration barriers in aluminum and tin, they should be more suitable to be used as AFB anodes, compared to copper, silver, and lead. The Li diffusion facilitation in copper with a certain number of vacancies is proposed to explain why the use of copper with a thickness≤100 nm as the protective coating on the anode improves the lifetime of the batteries. We show that the volume expansion has a positive effect on Li diffusion via mechanical–electrochemical coupling. Namely, the volume expansion caused by Li diffusion will further induce stress which in turn affects the diffusion. These findings not only provide in-depth insight into the operating principle of AFBs, but also open a new route toward design of improved anode through utilizing the positive effect of mechanical–electrochemical coupling. | 庄严 邹喆乂 吕浡 李亚捷 王达 Maxim Avdeev 施思齐 | 2020 | Chinese Physics B2020，29，6： | 3 |
| 3 | Crystal and magnetic structure of ( 1 ? x ) BiFeO 3 – x SrTiO 3 ( x =0.2, 0.3, 0.4 and 0.8)显示文摘 | D.J. Goossens C.J. Weekes Maxim Avdeev W.D. Hutchison | 2013 | Journal of Solid State Chemistry2013，，： | 1 |
| 4 | Transitions between P21, P63 ( A), and P6a 22 modifications of SrAI2 04 by in situ high-temperature X-ray and neutron diffraction显示文摘 | Avdeev Maxim Yakovlev Sergey Yaremehenko Aleksey A | 2007 | Journal of Solid State Chemistry2007，180，： | 1 |
| 5 | Domain knowledge discovery from abstracts of scientific literature on Nickel-based single crystal superalloys显示文摘Despite the huge accumulation of scientific literature,it is inefficient and laborious to manually search it for useful information to investigate structure-activity relationships.Here,we propose an efficient text-mining framework for the discovery of credible and valuable domain knowledge from abstracts of scientific literature focusing on Nickel-based single crystal superalloys.Firstly,the credibility of abstracts is quantified in terms of source timeliness,publication authority and author’s academic standing.Next,eight entity types and domain dictionaries describing Nickel-based single crystal superalloys are predefined to realize the named entity recognition from the abstracts,achieving an accuracy of 85.10%.Thirdly,by formulating 12 naming rules for the alloy brands derived from the recognized entities,we extract the target entities and refine them as domain knowledge through the credibility analysis.Following this,we also map out the academic cooperative“Author-Literature-Institute”network,characterize the generations of Nickel-based single crystal superalloys,as well as obtain the fractions of the most important chemical elements in superalloys.The extracted rich and diverse knowledge of Nickel-based single crystal superalloys provides important insights toward understanding the structure-activity relationships for Nickel-based single crystal superalloys and is expected to accelerate the design and discovery of novel superalloys. | LIU Yue DING Lin YANG ZhengWei GE XianYuan LIU DaHui LIU Wei YU Tao AVDEEV Maxim SHI SiQi | 2023 | Science China(Technological Sciences)2023，66，6： | 1 |
| 6 | Generative artificial intelligence and its applications in materials science:Current situation and future perspectives显示文摘Generative Artificial Intelligence(GAI)is attracting the increasing attention of materials community for its excellent capability of generating required contents.With the introduction of Prompt paradigm and reinforcement learning from human feedback(RLHF),GAI shifts from the task-specific to general pattern gradually,enabling to tackle multiple complicated tasks involved in resolving the structure-activity relationships.Here,we review the development status of GAI comprehensively and analyze pros and cons of various generative models in the view of methodology.The applications of task-specific generative models involving materials inverse design and data augmentation are also dissected.Taking ChatGPT as an example,we explore the potential applications of general GAI in generating multiple materials content,solving differential equation as well as querying materials FAQs.Furthermore,we summarize six challenges encountered for the use of GAI in materials science and provide the corresponding solutions.This work paves the way for providing effective and explainable materials data generation and analysis approaches to accelerate the materials research and development. | Yue Liu Zhengwei Yang Zhenyao Yu Zitu Liu Dahui Liu Hailong Lin Mingqing Li Shuchang Ma Maxim Avdeev Siqi Shi | 2023 | Journal of Materiomics2023，9，4： | 1 |
| 7 | Efficient potential-tuning strategy through p-type doping for designing cathodes with ultrahigh energy density显示文摘Designing new cathodes with high capacity and moderate potential is the key to breaking the energy density ceiling imposed by current intercalation chemistry on rechargeable batteries.The carbonaceous materials provide high capacities but their low potentials limit their application to anodes.Here,we show that Fermi level tuning by p-type doping can be an effective way of dramatically raising electrode potential.We demonstrate that Li(Na)BCF2/L i(Na)B2C2F2 exhibit such change in Fermi level,enabling them to accommodate Li^+(Na^+)with capacities of 290-400(250-320)mAh g^-1 at potentials of 3.4-3.7(2.7-2.9)V,delivering ultrahigh energy densities of 1000-1500 Wh kg^-1.This work presents a new strategy in tuning electrode potential through electronic band structure engineering. | Zhiqiang Wang Da Wang Zheyi Zou Tao Song Dixing Ni Zhenzhu Li Xuecheng Shao Wanjian Yin Yanchao Wang Wenwei Luo Musheng Wu Maxim Avdeev Bo Xu Siqi Shi Chuying Ouyang Liquan Chen | 2020 | National Science Review2020，7，11： | 1 |
| 8 | Data quantity governance for machine learning in materials science显示文摘Data-driven machine learning(ML)is widely employed in the analysis of materials structure-activity relationships,performance optimization and materials design due to its superior ability to reveal latent data patterns and make accurate prediction.However,because of the laborious process of materials data acquisition,ML models encounter the issue of the mismatch between a high dimension of feature space and a small sample size(for traditional ML models)or the mismatch between model parameters and sample size(for deep-learning models),usually resulting in terrible performance.Here,we review the efforts for tackling this issue via feature reduction,sample augmentation and specific ML approaches,and show that the balance between the number of samples and features or model parameters should attract great attention during data quantity governance.Following this,we propose a synergistic data quantity governance flow with the incorporation of materials domain knowledge.After summarizing the approaches to incorporating materials domain knowledge into the process of ML,we provide examples of incorporating domain knowledge into governance schemes to demonstrate the advantages of the approach and applications.The work paves the way for obtaining the required high-quality data to accelerate materials design and discovery based on ML. | Yue Liu Zhengwei Yang Xinxin Zou Shuchang Ma Dahui Liu Maxim Avdeev Siqi Shi | 2023 | National Science Review2023，10，7： | 0 |
| 9 | Grain size and structure distortion characterization of α-MgAgSb thermoelectric material by powder diffraction显示文摘Nanostructuring, structure distortion, and/or disorder are the main manipulation techniques to reduce the lattice thermal conductivity and improve the figure of merit of thermoelectric materials. A single-phase α-MgAgSb sample, MgAg0.97Sb0.99, with high thermoelectric performance in near room temperature region was synthesized through a high-energy ball milling with a hot-pressing method. Here, we report the average grain size of 24–28 nm and the accurate structure distortion, which are characterized by high-resolution neutron diffraction and synchrotron x-ray diffraction with Rietveld refinement data analysis. Both the small grain size and the structure distortion have a contribution to the low lattice thermal conductivity in MgAg0.97Sb0.99. | 李西阳 张志刚 何伦华 Maxim Avdeev 任洋 赵怀周 王芳卫 | 2020 | Chinese Physics B2020，29，10： | 0 |
| 10 | Auto-MatRegressor材料性能自动预测器:解放材料机器学习'调参师'显示文摘机器学习因其能够快速、精准拟合数据的潜在模式而被广泛应用于材料构效关系研究。然而,材料科学家往往需要进行繁琐的模型选择及参数寻优才能构建出高精度预测模型,为了解放材料机器学习'调参师',本文研发了基于元学习的材料性能自动预测器,采集了60份文献公开数据集与60份标准数据集,基于此训练18种常用回归算法并获得其预测性能,定义与计算了27个刻画数据集特点的元特征,以此构建了一份蕴含建模经验的元数据集;同时,创建了表征数据集所属材料类型的类别树,将其嵌入基于距离的元学习算法,进一步耦合贝叶斯优化算法,实现领域知识和元数据协同驱动下的自动算法推荐和模型参数确定,实验结果表明,材料科学家仅需为新材料性能预测任务提供数据集,便可利用该预测器高效地构建具有与文献报道相当或更高预测精度的机器学习模型. | 刘悦 王双燕 杨正伟 Maxim Avdeev 施思齐 | 2023 | Science Bulletin2023，68，12： | 0 |
| 11 | A customized strategy to design intercalation-type Li-free cathodes for all-solid-state batteries显示文摘Pairing Li-free transition-metal-based cathodes(MX) with Li-metal anodes is an emerging trend to overcome the energy-density limitation of current rechargeable Li-ion technology.However,the development of practical Li-free MX cathodes is plagued by the existing notion of low voltage due to the long-term overlo oked voltage-tuning/phase-stability competition.Here,we propose a p-type alloying strategy involving three voltage/phase-evolution stages,of which each of the varying trends are quantitated by two improved ligand-field descriptors to balance the above contradiction.Following this,an intercalation-type 2H-V_(1.75)Cr_(0.25) S_4 cathode tuned from layered MX_(2) family is successfully designed,which possesses an energy density of 554.3 Wh kg^(-1) at the ele ctrode level accompanied by interfacial compatibility with sulfide solid-state ele ctrolyte.The propos al of this class of materials is expected to break free from scarce or high-cost transition-metal(e.g.Co and Ni) reliance in current commercial cathodes.Our experiments further confirm the voltage and energy-density gains of 2H-V_(1.75)Cr_(0.25)S_4.This strategy is not limited to specific Li-free cathodes and offers a solution to achieve high voltage and phase stability simultaneously. | Da Wang Jia Yu Xiaobin Yin Sen Shao Qianqian Li Yanchao Wang Maxim Avdeev Liquan Chen Siqi Shi | 2023 | National Science Review2023，10，3： | 0 |
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