研究者詳細

顔写真

ジヤ　シユエ
Jia Xue
Jia Xue
所属
高等研究機構材料科学高等研究所　数学連携グループ
職名
助教
学位

  • Ｐｈ．Ｄ．（Harbin Institute of Technology）

  • Ｍ．Ｓ．（Harbin Institute of Technology）

e-Rad 研究者番号
40973045

経歴 1

  • 2022年8月 ～ 継続中
    Advanced Institute for Materials Research, Tohoku University　Assistant Professor

学歴 3

  • Harbin Institute of Technology　Materials Science and Engineering

    2018年9月 ～ 2022年7月

  • Harbin Institute of Technology　Materials Science and Engineering

    2015年9月 ～ 2017年7月

  • Jiamusi University　Materials Science and Engineering

    2010年9月 ～ 2014年7月

所属学協会 3

  • Junior Editorial Board Member, AI Agent

    2025年9月 ～ 継続中

  • Junior Editorial Board Member, Journal of Materials Informatics

    2025年6月 ～ 継続中

  • Academic Assistant Editor, Journal of Materials Informatics

    2023年7月 ～

研究キーワード 1

  • Data-mining in materials science, functional materials, electrocatalyst, thermoelectric materials

研究分野 3

  • エネルギー / 地球資源工学、エネルギー学 /

  • ナノテク・材料 / エネルギー化学 /

  • ナノテク・材料 / 基礎物理化学 /

受賞 7

  1. 2024 Outstanding Contribution Award as an Academic Assistant Editor of Journal of Materials Informatics

    2025年2月

  2. 東北大学プロミネントリサーチフェロー

    2024年7月　東北大学

  3. Emerging Investigators in Materials Chemistry

    2024年4月　Royal Society of Chemistry,UK

  4. Best poster awards: second place

    2023年2月　6th Forum of Materials Genome Engineering　Semi-Supervised Learning Guided for Exploration of High-Performance Thermoelectric Materials

  5. Best poster awards: first place

    2021年10月　4th Forum of Materials Genome Engineering　Designing 1-2-2 Zintl Thermoelectric Materials via Machine Learning with the New Thermoelectric Quality Factor

  6. Best poster awards

    2021年8月　19th National Phase Diagram Academic Conference and International Symposium on Material Design　Unsupervised Machine Learning for Discovery of Promising Half-Heusler Thermoelectric Materials

  7. Best poster awards

    2021年7月　2nd Doctoral Student Academic Forum, Harbin Institute of Technology (Shenzhen)　Supervised learning for predicting thermoelectric quality factor to accelerate the design of 1-2-2 type Zintl thermoelectric materials

︎全件表示 ︎最初の5件までを表示

論文 32

  1. Universal Catalyst Design Framework for Electrochemical Hydrogen Peroxide Synthesis Facilitated by Local Atomic Environment Descriptors

    Zhijian Liu, Yan Liu, Yuqi Zhang, Yeyu Deng, Zhong Zheng, Ruth Knibbe, Tianxiang Gao, Mingzhe Li, Ziye Wang, Bingqian Zhang, Xue Jia, Di Zhang, Heng Liu, Xuqiang Shao, Zhengyang Gao, Li Wei, Hao Li, Weijie Yang

    Angewandte Chemie International Edition　2025年12月8日

    出版者・発行元： Wiley

    DOI： 10.1002/anie.202518027 　

    ISSN：1433-7851

    eISSN：1521-3773

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    Abstract A universal design framework for high‐performance catalysts remains challenging due to diverse structures and active sites. We developed a framework integrating weighted atom‐centered symmetry function (wACSF) descriptors with machine learning, microkinetic modeling, and high‐throughput screening. The wACSF descriptors unify geometric and chemical characteristics of active sites across different catalyst families. ML models trained on wACSF accurately predicted adsorption free energies of hydroxyl (ΔG OH * , R 2 = 0.84) and oxygen (ΔG O* , R 2 = 0.91) for intermetallic alloys, metal oxides, perovskites, and single‐atom catalysts in the two‐electron water oxidation reaction (2e − WOR). Density functional theory and microkinetic modeling yielded a universal 2e − WOR volcano model that agreed well with experiments. High‐throughput screening with ML‐predicted ΔG OH* identified LiScO 2 , which achieved 90% H 2 O 2 Faradaic efficiency at 2.2 V vs. reversible hydrogen electrode (RHE) with 168‐hour stability (82%–86% retention). Experimental activity (log( j ) = 1.56) matched theoretical predictions (log( j ) = 1.28) within 5% deviation at 2.4 V_RHE. This universal framework provides a general paradigm for rational catalyst design and is implemented in the Digital Catalysis Platform ( DigCat ), enabling efficient discovery across diverse material classes and electrochemical reactions.

  2. Machine learning-driven advances in water treatment

    Zhen Qiu, Zechao Zhuang, Xue Jia, Hao Li, Cheng Chen, Jinchao Shen, Bing Yu

    Journal of Environmental Chemical Engineering　13　(6)　120441-120441　2025年12月

    出版者・発行元： Elsevier BV

    DOI： 10.1016/j.jece.2025.120441 　

    ISSN：2213-3437

  3. Design of metal-supported MgO catalysts for electrochemical CO2 reduction to formic acid via the Mott–Schottky effect

    Shangqing Zhao, Yuhang Wang, Heng Liu, Xue Jia, Yizhou Zhang, Linda Zhang, Bo Da, Qiang Wang, Huiling Zheng, Hao Li, Wenying Li

    Journal of Catalysis　116502-116502　2025年10月

    出版者・発行元： Elsevier BV

    DOI： 10.1016/j.jcat.2025.116502 　

    ISSN：0021-9517

  4. Artificial intelligence technology in environmental research and health: Development and prospects

    Zhuling Guo, Lei Huang, Jia Yan, Hongguo Zhang, Xue Jia, Meng Li, Hao Li

    2025年9月

    DOI： 10.1016/j.envint.2025.109788 　

  5. Application of an Explainable Machine Learning to CO2 Methanation for Optimal Design Nickel-Based Catalysts

    Jiayi Zhang, Xue Jia, Hao Li, Fukui Xiao, Qiang Wang, Ning Zhao

    ACS Sustainable Chemistry & Engineering　13　(35)　14278-14289　2025年8月22日

    出版者・発行元： American Chemical Society (ACS)

    DOI： 10.1021/acssuschemeng.5c02957 　

    ISSN：2168-0485

    eISSN：2168-0485

  6. Enhanced extrapolative machine learning for designing high-performance multi-principal-element superalloys

    Qiu-Ling Tao, Long-Ke Bao, Jia-Wen Cao, Rong-Pei Shi, Yi-Lu Zhao, Tao Yang, Xue Jia, Zhi-Fu Yao, Xing-Jun Liu

    Rare Metals　2025年7月29日

    出版者・発行元： Springer Science and Business Media LLC

    DOI： 10.1007/s12598-025-03434-4 　

    ISSN：1001-0521

    eISSN：1867-7185

  7. Advancing electrocatalyst discovery through the lens of data science: State of the art and perspectives

    Xue Jia, Tianyi Wang, Di Zhang, Xuan Wang, Heng Liu, Liang Zhang, Hao Li

    Journal of Catalysis　447　116162-116162　2025年7月

    出版者・発行元： Elsevier BV

    DOI： 10.1016/j.jcat.2025.116162 　

    ISSN：0021-9517

  8. Bridging Theory and Experiment: Machine Learning Potential‐Driven Insights into pH‐Dependent CO₂ Reduction on Sn‐Based Catalysts

    Yuhang Wang, Zelin Wu, Yingfang Jiang, Di Zhang, Qiang Wang, Congwei Wang, Huihui Li, Xue Jia, Jun Fan, Hao Li

    Advanced Functional Materials　2025年6月26日

    出版者・発行元： Wiley

    DOI： 10.1002/adfm.202506314 　

    ISSN：1616-301X

    eISSN：1616-3028

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    Abstract Sn‐based materials are among the most promising catalysts for CO2 reduction reaction (CO2RR) to formic acid. However, the complex electrochemistry‐induced surface reconstruction under negative potentials has hindered the precise elucidation of the structure‐performance relationship. Herein, machine learning potential (MLP) is employed to accelerate molecular dynamics (MD) simulations, and pH‐field coupled microkinetic modelling is perfromed to unravel the pH dependence of CO2RR at the reversible hydrogen electrode (RHE) scale. Encouragingly, the developed MLP reveals that SnO2 adopts a nanorod‐like morphology, accurately reproducing experimentally observed reconstruction phenomena. Additionally, SnS2 prefers to form a rougher surface. Leveraging the precisely determined reconstructed surface, the exciting pH‐dependent behavior of Sn‐based catalysts is highlighted: the increase of pH will cause a left‐shift in the CO2RR volcano and ultimately enhance the catalyst's activity. Most importantly, the excellent agreement between the theoretical simulations and our subsequent experimental measurements validates the accuracy of the simulations in terms of turnover frequencies, providing a clear benchmarking analysis between experiments and the MLP‐MD‐assisted pH‐field coupled microkinetic modelling. This work not only offers a valuable MLP‐based approach for studying surface reconstructions, but also provides new guidance for the design of high‐performance complex catalysts for CO2RR.

  9. Data-Driven Strategies for Designing Multicomponent Molten Catalysts to Accelerate the Industrialization of Methane Pyrolysis

    Yuanzheng Chen, Xuxuan Huang, Yangdong He, Qian Liu, Junmei Du, Wei Yang, Wenhan Wang, Di Zhang, Xue Jia, Hongyan Wang, Yongliang Tang, Qingkai Yu, Seok Ki Kim, Hao Li

    ACS Catalysis　15　(13)　11003-11012　2025年6月11日

    出版者・発行元： American Chemical Society (ACS)

    DOI： 10.1021/acscatal.5c02415 　

    ISSN：2155-5435

    eISSN：2155-5435

  10. Closed-Loop Framework for Discovering Stable and Low-Cost Bifunctional Metal Oxide Catalysts for Efficient Electrocatalytic Water Splitting in Acid

    Xue Jia, Zihan Zhou, Fangzhou Liu, Tianyi Wang, Yuhang Wang, Di Zhang, Heng Liu, Yong Wang, Songbo Ye, Koji Amezawa, Li Wei, Hao Li

    Journal of the American Chemical Society　2025年5月19日

    出版者・発行元： American Chemical Society (ACS)

    DOI： 10.1021/jacs.5c04079 　

    ISSN：0002-7863

    eISSN：1520-5126

  11. Gd‐Induced Oxygen Vacancy Creation Activates Lattice Oxygen Oxidation for Water Electrolysis

    Yong Wang, Yadong Liu, Sijia Liu, Yunpu Qin, Jianfang Liu, Xue Jia, Qiuling Jiang, Xuan Wang, Yongzhi Zhao, Luan Liu, Hongru Liu, Hong Zhao, Yirui Jiang, Dong Liang, Haoyang Wu, Baorui Jia, Xuanhui Qu, Hao Li, Mingli Qin

    Advanced Functional Materials　2025年2月26日

    出版者・発行元： Wiley

    DOI： 10.1002/adfm.202500118 　

    ISSN：1616-301X

    eISSN：1616-3028

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    Abstract As a key reaction in water electrolysis and fuel cells, the oxygen evolution reaction (OER) involves a sluggish four‐electron proton transfer process. Understanding the OER pathways and kinetics is critical for designing efficient electrocatalysts. In this study, through density functional theory (DFT) calculations, it is demonstrated that the incorporation of Gd into Fe‐doped NiO elevates the O 2p band center and generates more unoccupied oxygen states. Furthermore, Gd promotes the formation of oxygen vacancies, which, together, enhance the lattice oxygen oxidation mechanism (LOM) pathway for the OER. The adsorption‐free energy diagrams confirm that Gd doping significantly lowers the theoretical overpotentials at both the Fe and Ni sites in Fe‐doped NiO, thereby improving OER activity. Based on these findings, Gd and Fe co‐doped NiO ultrathin nanosheets are synthesized via spray combustion. As an OER catalyst, the material exhibited a low overpotential of 227 mV, which is 40 mV lower than that of Fe‐doped NiO, and demonstrated long‐term catalytic stability for over 150 h. In an anion exchange membrane water electrolysis system, Gd and Fe co‐doped NiO exhibited stable performance for more than 120 h at a current density of 20 mA cm−2.

  12. Electrochemical CO2 Reduction on SnO: Insights into C1 Product Dynamic Distribution and Reaction Mechanisms

    Zhongyuan Guo, Tianyi Wang, Heng Liu, Xue Jia, Di Zhang, Li Wei, Jiang Xu, Hao Li

    ACS Catalysis　3173-3183　2025年2月6日

    出版者・発行元： American Chemical Society (ACS)

    DOI： 10.1021/acscatal.4c07987 　

    ISSN：2155-5435

    eISSN：2155-5435

  13. Machine learning strategies for small sample size in materials science

    Qiuling Tao, JinXin Yu, Xiangyu Mu, Xue Jia, Rongpei Shi, Zhifu Yao, Cuiping Wang, Haijun Zhang, Xingjun Liu

    Science China Materials　2025年1月2日

    出版者・発行元： Springer Science and Business Media LLC

    DOI： 10.1007/s40843-024-3204-5 　

    ISSN：2095-8226

    eISSN：2199-4501

  14. Physically interpretable descriptors drive the materials design of metal hydrides for hydrogen storage

    Seong-Hoon Jang, Di Zhang, Hung Ba Tran, Xue Jia, Kiyoe Konno, Ryuhei Sato, Shin-ichi Orimo, Hao Li

    Chemical Science　2025年

    出版者・発行元： Royal Society of Chemistry (RSC)

    DOI： 10.1039/d5sc07296d 　

    ISSN：2041-6520

    eISSN：2041-6539

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    The design of metal hydrides for hydrogen storage is hindered by the large compositional space and intricate structure–property relationships, which can be addressed using a white-box interpretable model.

  15. Decoding pH-dependent electrocatalysis through electric field models and microkinetic volcanoes

    Songbo Ye, Yuhang Wang, Heng Liu, Di Zhang, Xue Jia, Linda Zhang, Yizhou Zhang, Akichika Kumatani, Hitoshi Shiku, Hao Li

    Journal of Materials Chemistry A　2025年

    出版者・発行元： Royal Society of Chemistry (RSC)

    DOI： 10.1039/d5ta06105a 　

    ISSN：2050-7488

    eISSN：2050-7496

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    This perspective highlights advances in capturing pH-dependent surface structures, reactivity, and mechanisms via electric field-based methods.

  16. Divergent Activity Shifts of Tin‐Based Catalysts for Electrochemical CO2 Reduction: pH‐Dependent Behavior of Single‐Atom Versus Polyatomic Structures

    Yuhang Wang, Di Zhang, Bin Sun, Xue Jia, Linda Zhang, Hefeng Cheng, Jun Fan, Hao Li

    Angewandte Chemie International Edition　64　(8)　2024年12月17日

    出版者・発行元： Wiley

    DOI： 10.1002/anie.202418228 　

    ISSN：1433-7851

    eISSN：1521-3773

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    Abstract Tin (Sn)‐based catalysts have been widely studied for electrochemical CO2 reduction reaction (CO2RR) to produce formic acid, but the intricate influence of the structural sensitivity in single‐atom Sn (e.g., Sn−N−C) and polyatomic Sn (e.g., SnOx and SnSx; x=1,2) on their pH‐dependent performance remains enigmatic. Herein, we integrate large‐scale data mining (with >2,300 CO2RR catalysts from available experimental literature during the past decade), ab initio computations, machine learning force field accelerated molecular dynamic simulations, and pH‐field coupled modelling to unravel their pH dependence. We reveal a fascinating contrast: the electric field response of the binding strength of *OCHO on Sn−N4−C and polyatomic Sn exhibits opposite behaviors due to their differing dipole moment changes upon *OCHO formation. Such response leads to an intriguing opposite pH‐dependent volcano evolution for Sn−N4−C and polyatomic Sn. Subsequent experimental validations of turnover frequency and current density under both neutral and alkaline conditions well aligned with our theoretical predictions. Most importantly, our analysis suggests the necessity of distinct optimization strategies for *OCHO binding energy on different types of Sn‐based catalysts.

  17. Divergent Activity Shifts of Tin‐Based Catalysts for Electrochemical CO2 Reduction: pH‐Dependent Behavior of Single‐Atom versus Polyatomic Structures

    Yuhang Wang, Di Zhang, Bin Sun, Xue Jia, Linda Zhang, Hefeng Cheng, Jun Fan, Hao Li

    Angewandte Chemie　2024年11月28日

    出版者・発行元： Wiley

    DOI： 10.1002/ange.202418228 　

    ISSN：0044-8249

    eISSN：1521-3757

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    Tin (Sn)‐based catalysts have been widely studied for electrochemical CO2 reduction reaction (CO2RR) to produce formic acid, but the intricate influence of the structural sensitivity in single‐atom Sn (e.g., Sn‐N‐C) and polyatomic Sn (e.g., SnOx and SnSx; x=1,2) on their pH‐dependent performance remains enigmatic. Herein, we integrate large‐scale data mining (with >2,300 CO2RR catalysts from available experimental literature during the past decade), ab initio computations, machine learning force field accelerated molecular dynamic simulations, and pH‐field coupled microkinetic modelling to unravel their pH dependence. We reveal a fascinating contrast: the electric field response of the binding strength of *OCHO on Sn‐N4‐C and polyatomic Sn exhibits opposite behaviors due to their differing dipole moment changes upon *OCHO formation. Such response leads to an intriguing opposite pH‐dependent volcano evolution for Sn‐N4‐C and polyatomic Sn. Subsequent experimental validations of turnover frequency and current density under both neutral and alkaline conditions well aligned with our theoretical predictions. Most importantly, our analysis suggests the necessity of distinct optimization strategies for *OCHO binding energy on different types of Sn‐based catalysts.

  18. Data-Driven Viewpoint for Developing Next-Generation Mg-Ion Solid-State Electrolytes

    Fang-Ling Yang, Ryuhei Sato, Eric Jian-Feng Cheng, Kazuaki Kisu, Qian Wang, Xue Jia, Shin-ichi Orimo, Hao Li

    Journal of Electrochemistry　30　(7)　2024年7月28日

    出版者・発行元： (Xiamen: Dianhuaxue Bianjibu) Editorial Office of Journal of Electrochemistry, Xiamen University

    DOI： 10.61558/2993-074x.3461 　

    eISSN：2993-074X

  19. Origin of the Activity of Electrochemical Ozone Production over Rutile PbO2 Surfaces

    Jin-Tao Jiang, Zhongyuan Guo, Shao-Kang Deng, Xue Jia, Heng Liu, Jiang Xu, Hao Li, Li-Hua Cheng

    ChemSusChem　2024年5月24日

    出版者・発行元： Wiley

    DOI： 10.1002/cssc.202400827 　

    ISSN：1864-5631

    eISSN：1864-564X

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    Ozonation water treatment technology has attracted increasing attention due to its environmental benign and high efficiency. Rutile PbO2 is a promising anode material for electrochemical ozone production (EOP). However, the reaction mechanism underlying ozone production catalyzed by PbO2 was rarely studied and not well‐understood, which was in part due to the overlook of the electrochemistry‐driven formation of oxygen vacancy (OV) of PbO2. Herein, we unrevealed the origin of the EOP activity of PbO2 starting from the electrochemical surface state analysis using density functional theory (DFT) calculations, activity analysis, and catalytic volcano modeling. Interestingly, we found that under experimental EOP potential (i.e., a potential around 2.2 V vs. reversible hydrogen electrode), OV can still be generated easily on PbO2 surfaces. Our subsequent kinetic and thermodynamic analyses show that these OV sites on PbO2 surfaces are highly active for the EOP reaction through an interesting atomic oxygen (O*)‐O2 coupled mechanism. In particular, rutile PbO2(101) with the “in‐situ” generated OV exhibited superior EOP activities, outperforming (111) and (110). Finally, by catalytic modeling, we found that PbO2 is close to the theoretical optimum of the reaction, suggesting a superior EOP performance of rutile PbO2. All these analyses are in good agreement with experimental observations.

  20. Data Mining of Stable, Low-Cost Metal Oxides as Potential Electrocatalysts

    Xue Jia, Hao Li

    Artificial Intelligence Chemistry　100065-100065　2024年4月

    出版者・発行元： Elsevier BV

    DOI： 10.1016/j.aichem.2024.100065 　

    ISSN：2949-7477

  21. Dealing with the big data challenges in AI for thermoelectric materials

    Xue Jia, Alex Aziz, Yusuke Hashimoto, Hao Li

    Science China Materials　67　(4)　1173-1182　2024年3月8日

    出版者・発行元： Springer Science and Business Media LLC

    DOI： 10.1007/s40843-023-2777-2 　

    ISSN：2095-8226

    eISSN：2199-4501

  22. Machine Learning Enabled Exploration of Multicomponent Metal Oxides for Catalyzing Oxygen Reduction in Alkaline Media

    Xue Jia, Hao Li

    Journal of Materials Chemistry A　2024年

    出版者・発行元： Royal Society of Chemistry (RSC)

    DOI： 10.1039/d4ta01884b 　

    ISSN：2050-7488

    eISSN：2050-7496

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    Low-cost metal oxides have emerged as promising candidates used as electrocatalysts for oxygen reduction reaction (ORR) due to their remarkable stability under oxidizing conditions, particularly in alkaline media. Recent studies...

  23. Identifying Stable Electrocatalysts Initialized by Data Mining: Sb2WO6 for Oxygen Reduction

    Jia, Xue, Yu, Zixun, Liu, Fangzhou, Liu, Heng, Zhang, Di, Campos dos Santos, Egon, Zheng, Hao, Hashimoto, Yusuke, Chen, Yuan, Wei, Li, Li, Hao

    Advanced Science　n/a　(n/a)　2305630-2305630　2023年12月7日

    DOI： 10.1002/advs.202305630 　

  24. Advancing thermoelectric materials discovery through semi-supervised learning and high-throughput calculations

    Xue Jia, Honghao Yao, Zhijie Yang, Jianyang Shi, Jinxin Yu, Rongpei Shi, Haijun Zhang, Feng Cao, Xi Lin, Jun Mao, Cuiping Wang, Qian Zhang, Xingjun Liu

    Applied Physics Letters　2023年11月13日

    DOI： 10.1063/5.0175233 　

  25. A dynamic database of solid-state electrolyte (DDSE) picturing all-solid-state batteries

    Fangling Yang, Egon Campos dos Santos, Xue Jia, Ryuhei Sato, Kazuaki Kisu, Yusuke Hashimoto, Shin-ichi Orimo, Hao Li

    Nano Materials Science　2023年9月

    出版者・発行元： Elsevier {BV}

    DOI： 10.1016/j.nanoms.2023.08.002 　

    ISSN：2589-9651

    eISSN：2589-9651

  26. Explore the Ionic Conductivity Trends on B12H12 Divalent Closo-Type Complex Hydride Electrolytes

    Egon Campos dos Santos, Ryuhei Sato, Kazuaki Kisu, Kartik Sau, Xue Jia, Fangling Yang, Shin-ichi Orimo, Hao Li

    Chemistry of Materials　35　(15)　5996-6004　2023年8月8日

    出版者・発行元： American Chemical Society ({ACS})

    DOI： 10.1021/acs.chemmater.3c00975 　

    ISSN：0897-4756 1520-5002

    eISSN：1520-5002

  27. The surface states of transition metal X-ides under electrocatalytic conditions

    Heng Liu, Xue Jia, ANG CAO, Li Wei, Carmine D'Agostino, Hao Li

    The Journal of Chemical Physics　158　(12)　124705-124705　2023年3月28日

    出版者・発行元： {AIP} Publishing

    DOI： 10.1063/5.0147123 　

    ISSN：0021-9606 1089-7690

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    <jats:p> Due to conversion equilibrium between solvent and H- and O-containing adsorbates, the true surface state of a catalyst under a particular electrochemical condition is often overlooked in electrocatalysis research. Herein, by using surface Pourbaix analysis, we show that many electrocatalytically active transition metal X-ides (e.g., oxides, nitrides, carbides, and hydroxides) tend to possess the surface states different from their pristine stoichiometric forms under the pH and potential of interest due to water dissociation or generation. Summarizing the density functional theory calculated surface Pourbaix diagrams of 14 conditionally stable transition metal X-ide materials, we found that some of these surfaces tend to be covered by O-containing adsorbates at a moderate or high potential, while vacancies or H-covered surfaces may form at a low potential. These results suggest the possibility of poisoning or creation of surface sites beyond the pristine surface, implying that the surface state under reaction conditions (pH and potentials) needs to be considered before the identification and analysis of active sites of a transition metal X-ide catalyst. In addition, we provide an explanation of the observed theory and experiment discrepancy that some transition metal X-ides are “more stable in experiment than in theory.” Based on our findings, we conclude that analyzing the surface state of transition metal X-ide electrocatalysts by theoretical calculations (e.g., surface Pourbaix diagram analysis), in situ/ operando and post-reaction experiments are indispensable to accurately understand the underlying catalytic mechanisms. </jats:p>

  28. Rapid accomplishment of strength/ductility synergy for additively manufactured Ti-6Al-4V facilitated by machine learning

    Zhifu Yao, Xue Jia, Jinxin Yu, Mujin Yang, Chao Huang, Zhijie Yang, Cuiping Wang, Tao Yang, Shuai Wang, Rongpei Shi, Jun Wei, Xingjun Liu

    Materials & Design　2022年12月

  29. Unsupervised machine learning for discovery of promising half-Heusler thermoelectric materials

    Xue Jia, Yanshuai Deng, Xin Bao, Honghao Yao, Shan Li, Zhou Li, Chen Chen, Xinyu Wang, Jun Mao, Feng Cao, Jiehe Sui, Junwei Wu, Cuiping Wang, Qian Zhang, Xingjun Liu

    npj Computational Materials　8　(1)　2022年12月

    DOI： 10.1038/s41524-022-00723-9 　

    eISSN：2057-3960

  30. Martensite colony engineering: A novel solution to realize the high ductility in full martensitic 3D-printed Ti alloys

    Zhifu Yao, Tao Yang, Mujin Yang, Xue Jia, Chenglei Wang, Jinxin Yu, Zhou Li, Heyu Han, Weihong Liu, Guoqiang Xie, Shuiyuan Yang, Qian Zhang, Cuiping Wang, Shuai Wang, Xingjun Liu

    Materials and Design　215　2022年3月

    DOI： 10.1016/j.matdes.2022.110445 　

    ISSN：0264-1275

    eISSN：1873-4197

  31. Band convergence and phonon engineering to optimize the thermoelectric performance of CaCd<inf>2</inf>Sb<inf>2</inf>

    Zongwei Zhang, Honghao Yao, Xue Jia, Xinyu Wang, Xiaofang Li, Chen Chen, Xi Lin, Jiehe Sui, Xingjun Liu, Jun Mao, Guoqiang Xie, Qian Zhang

    Applied Physics Letters　120　(4)　2022年1月24日

    DOI： 10.1063/5.0076087 　

    ISSN：0003-6951

  32. Using materials quality factor B<inf>ΔΕ</inf>∗ for design of thermoelectric materials with multiple bands

    X. Jia, S. Li, Z. Zhang, Y. Deng, X. Li, Y. Cao, Y. Yan, J. Mao, J. Yang, Q. Zhang, X. Liu

    Materials Today Physics　18　2021年5月

    DOI： 10.1016/j.mtphys.2021.100371 　

    eISSN：2542-5293

︎全件表示 ︎最初の5件までを表示

講演・口頭発表等 13

  1. Closed-Loop Framework for Discovering Stable and Low-Cost Metal Oxide Catalysts

    15th Early Career Researchers Ensemble Workshop　2025年12月12日

  2. Data-Driven Closed-Loop Frameworks for Energy Materials Design

    9th International Forum of Materials Genome Engineering Conference　2025年11月21日

  3. Data-Driven Closed-Loop Frameworks for Energy Materials Design 招待有り

    The 3rd World Materials Conference, in conjunction with the 9th World Materials Summit and the 26th IUMRS-ICA　2025年10月16日

  4. AI-Driven Tools to Accelerate Atomistic Simulations for Materials Design

    R7年度学術変革A「学習物理学」領域会議プログラム　2025年9月24日

  5. Data-Science for Energy Materials

    Seminar Talk at Institute of Coal Chemistry, Chinese Academy of Sciences　2025年4月27日

  6. Data-Driven Approaches for Designing Energy Materials

    Seminar Talk at Northwestern Polytechnical University, China (online)　2025年4月18日

  7. Data-Science for Energy Materials

    Seminar Talk at Tsinghua University　2024年1月3日

  8. Data-Science and AI for Energy Materials Exploration

    Symposium of crc-gp-mssp 2023　2023年11月28日

  9. Opportunities and Challenges in Identifying Stable Electrocatalysts Initialized by Data-mining

    Friday Teatime at AIMR, Tohoku university　2023年3月24日

  10. Screening Stable Oxides for Oxygen Electrocatalysis based on Data Mining 招待有り

    Xue Jia

    Research Talk with The University of Sydney　2022年12月13日

  11. Semi-Supervised Learning Guided for Exploration of High-Performance Thermoelectric Materials

    Xue Jia

    International FAIR-DI Conference on a FAIR Data Infrastructure for Materials Genomics　2022年7月15日

  12. Unsupervised Learning for the Exploration of Promising Half-Huesler Thermoelectric Materials

    Xue Jia

    Chinese Materials Conference (2021)　2021年7月12日

  13. Machine Learning-Based Method for the Exploration of Half-Heusler Thermoelectric Materials

    Xue Jia

    Chinese Materials Conference (2019)　2019年7月12日

︎全件表示 ︎最初の5件までを表示

共同研究・競争的資金等の研究課題 4

  1. Discover High-Performance Metal Carbide/Nitride Electrocatalysts Based on Catalysis Theory and Machine Learning

    提供機関：Ensemble Grants for Early Career Researchers 2025 in Tohoku University

    2025年6月 ～ 2026年3月

  2. Screening of transition metal oxide electrocatalysts in alkaline media based on data mining and theoretical analysis

    Xue Jia

    2023年3月 ～ 2025年3月

  3. マテリアルインフォマティクスによる廉価な遷移金属を利用した 酸性環境下での電極触媒の探索

    2022年12月 ～ 2024年12月

  4. Design of Cost-Effective Complex Metal Oxide Structures for CO2 Reduction Reaction

    提供機関：AIMR Fusion Research

    2023年4月 ～ 2024年3月

Works（作品等） 2

  1. Research Assistant

    Harbin Institute of Technology, Shenzhen

    2017年10月25日 ～ 2018年6月30日

    作品分類: その他

  2. Structure Engineer

    Kingclean Elctric Co

    2017年7月11日 ～ 2017年10月22日

    作品分類: その他