Details of the Researcher

PHOTO

Jia Xue
Section
Advanced Institute for Materials Research
Job title
Assistant Professor
Degree

  • Ｐｈ．Ｄ．（Harbin Institute of Technology）

  • Ｍ．Ｓ．（Harbin Institute of Technology）

e-Rad No.
40973045

Education 3

  • Harbin Institute of Technology　Materials Science and Engineering

    2018/09 - 2022/07

  • Harbin Institute of Technology　Materials Science and Engineering

    2015/09 - 2017/07

  • Jiamusi University　Materials Science and Engineering

    2010/09 - 2014/07

Professional Memberships 1

  • Academic Assistant Editor, Journal of Materials Informatics

    2023/07 -

Research Interests 1

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

Research Areas 3

  • Energy / Earth resource engineering, energy science / Materials informatics, Data mining, Machine learning

  • Nanotechnology/Materials / Energy chemistry / Electrocatalysts

  • Nanotechnology/Materials / Basic physical chemistry / Thermoelectric materials

Awards 6

  1. Prominent Research Fellow of Tohoku University

    2024/07　Tohoku university

  2. Emerging Investigators in Materials Chemistry

    2024/04　Royal Society of Chemistry,UK

  3. Best poster awards: second place

    2023/02　6th Forum of Materials Genome Engineering　Semi-Supervised Learning Guided for Exploration of High-Performance Thermoelectric Materials

  4. Best poster awards: first place

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

  5. Best poster awards

    2021/08　19th National Phase Diagram Academic Conference and International Symposium on Material Design　Unsupervised Machine Learning for Discovery of Promising Half-Heusler Thermoelectric Materials

  6. Best poster awards

    2021/07　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

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Papers 24

  1. 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　2025/07

    Publisher: Elsevier BV

    DOI： 10.1016/j.jcat.2025.116162 　

    ISSN： 0021-9517

  2. 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　2025/06/26

    Publisher: 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.

  3. 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　2025/06/11

    Publisher: American Chemical Society (ACS)

    DOI： 10.1021/acscatal.5c02415 　

    ISSN： 2155-5435

    eISSN： 2155-5435

  4. 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　2025/05/19

    Publisher: American Chemical Society (ACS)

    DOI： 10.1021/jacs.5c04079 　

    ISSN： 0002-7863

    eISSN： 1520-5126

  5. 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　2025/02/26

    Publisher: 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.

  6. 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　2025/02/06

    Publisher: American Chemical Society (ACS)

    DOI： 10.1021/acscatal.4c07987 　

    ISSN： 2155-5435

    eISSN： 2155-5435

  7. 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　2025/01/02

    Publisher: Springer Science and Business Media LLC

    DOI： 10.1007/s40843-024-3204-5 　

    ISSN： 2095-8226

    eISSN： 2199-4501

  8. 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)　2024/12/17

    Publisher: 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.

  9. 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　2024/11/28

    Publisher: 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.

  10. 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)　2024/07/28

    Publisher: (Xiamen: Dianhuaxue Bianjibu) Editorial Office of Journal of Electrochemistry, Xiamen University

    DOI： 10.61558/2993-074x.3461 　

    eISSN： 2993-074X

  11. 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　2024/05/24

    Publisher: 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.

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

    Xue Jia, Hao Li

    Artificial Intelligence Chemistry　100065-100065　2024/04

    Publisher: Elsevier BV

    DOI： 10.1016/j.aichem.2024.100065 　

    ISSN： 2949-7477

  13. 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　2024/03/08

    Publisher: Springer Science and Business Media LLC

    DOI： 10.1007/s40843-023-2777-2 　

    ISSN： 2095-8226

    eISSN： 2199-4501

  14. 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

    Publisher: 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...

  15. 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　2023/12/07

    DOI： 10.1002/advs.202305630 　

  16. 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　2023/11/13

    DOI： 10.1063/5.0175233 　

  17. 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　2023/09

    Publisher: Elsevier {BV}

    DOI： 10.1016/j.nanoms.2023.08.002 　

    ISSN： 2589-9651

    eISSN： 2589-9651

  18. 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　2023/08/08

    Publisher: American Chemical Society ({ACS})

    DOI： 10.1021/acs.chemmater.3c00975 　

    ISSN： 0897-4756 1520-5002

    eISSN： 1520-5002

  19. 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　2023/03/28

    Publisher: {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>

  20. 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　2022/12

  21. 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)　2022/12

    DOI： 10.1038/s41524-022-00723-9 　

    eISSN： 2057-3960

  22. 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　2022/03

    DOI： 10.1016/j.matdes.2022.110445 　

    ISSN： 0264-1275

    eISSN： 1873-4197

  23. 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)　2022/01/24

    DOI： 10.1063/5.0076087 　

    ISSN： 0003-6951

  24. 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　2021/05

    DOI： 10.1016/j.mtphys.2021.100371 　

    eISSN： 2542-5293

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Presentations 7

  1. Data-Science for Energy Materials

    2024/01/03

  2. Data-Science and AI for Energy Materials Exploration

    Symposium of crc-gp-mssp 2023　2023/11/28

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

    Friday Teatime at AIMR, Tohoku university　2023/03/24

  4. Screening Stable Oxides for Oxygen Electrocatalysis based on Data Mining Invited

    Xue Jia

    Research Talk with The University of Sydney　2022/12/13

  5. 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　2022/07/15

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

    Xue Jia

    Chinese Materials Conference (2021)　2021/07/12

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

    Xue Jia

    Chinese Materials Conference (2019)　2019/07/12

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Research Projects 3

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

    Xue Jia

    Offer Organization: Japan Society for the Promotion of Science

    2023/03 - 2025/03

  2. 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

    Offer Organization: ヒロセ財団

    2022/12 - 2024/12

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

    Offer Organization: Tohoku University

    2023/04 - 2024/03

Works 2

  1. Research Assistant

    Harbin Institute of Technology, Shenzhen

    2017/10/25 - 2018/06/30

    Type: Other

  2. Structure Engineer

    Kingclean Elctric Co

    2017/07/11 - 2017/10/22

    Type: Other