上海市浦江人才，天富平台工程高层论坛青年科学家奖获得者，材料学/力学硕士生导师💆🏻。2015年本科毕业于天津大学求是学部（导师👨🏻‍💻：刘永长教授）🙅🏼🥟；2018年和2021年分别于香港理工大学机械工程系获得硕士与博士学位（导师⛏：石三强讲席教授/张统一院士）；博士毕业后于香港理工大学深圳天富从事短期研究工作🌈，后前往哈尔滨工业大学开展博士后研究（合作导师◻️：张统一院士）😋，2023年9月加入天富平台开展教学科研工作🍮。目前是深圳市腐蚀与防护学会的专家委员会委员，Progress in Natural Science-Materials International🧎、Journal of Material Informatics、以及MGE Advances     杂志的青年编委，以及ACS Nano、Rare     Metals✋🏽、Science China Materials🧑🏻‍🦳、Materials & Design 等SCI期刊的独立审稿人🧑🏻‍🦰。

截止2024年底，已在Advanced Science、NPJ Computational     Materials🔅、Journal of Materials Science &     Technology等国内SCI知名杂志发表论文二十余篇，被引八百余次；此外还申请了十余项发明专利及软件著作权，并在天富平台工程高层论坛、美国MRS年会、美国TMS 年会等多个国内外会议发表报告。主持国家自然科学基金青年基金👩🏼‍⚕️、上海市科委等资助的天富平台工程相关项目5项✊🏼，作为研究骨干参与科技部重点研发计划课题、国家自然科学基金重点项目、以及深圳市技术攻关重点项目等重大科技项目。

研究方向

主要研究方向为“人工智能-专家知识”混合驱动的合金材料设计及极端环境下（航空及新一代核反应堆）的结构材料服役行为研究，每年计划招收1-2名硕士研究生🫘，欢迎对AI for Science 研究和金属结构材料感兴趣的同学联系报考。

代表性成果

截止2025年02月28日，共发表SCI论文28篇🦿，论文总被引826次，h指数11，i10指数13，论文发表详情见谷歌学术或Research Gate( https://www.researchgate.net/profile/Jie_Xiong29 )🎩。

●      Y. Yu, J. Xiong*, X. Wu, Q. Qian*, From Small Data Modeling to     Large Language Model Screening: A Dual-Strategy Framework for Materials     Intelligent Design. Advanced Science. (2024)

●      J. X. Ma, B. Cao, S. Y. Dong, Y. Tian, M. H. Wang, J. Xiong*, S     Sun*. MLMD: a programming-free AI platform to predict and design materials.     NPJ Computational Materials. (2024)

●      S. Y. Zhang, B Cao, T. H. Su, Y. Wu, Z. J. Feng, J. Xiong*, T.     Y. Zhang*. Crystallographic phase identifier of a convolutional     self-attention neural network (CPICANN) on powder diffraction patterns.     IUCrJ. (2024)

●      J. Xiong*,     B. W. Bai, H. R. Jiang, A. Faus-Golfe. Determinants of saturation magnetic     flux density in Fe-based metallic glasses: insights from machine-learning     models. Rare Metals. (2024)

●      H. Tian^#, J. Xiong^#, L. Zhao, J. Mei, Y.     Qi, J. W. Wu, K. K. Li, J. C. He*, T. Y. Zhang*. Enhanced vacuum brazing     joining between Ti-48Al-2Cr-2Nb/Ti-22Al-25Nb intermetallic alloys by     Zr-free Ti-based filler. Journal of Materials Research and Technology.     (2024)

●      Y. Wu, T. H. Su, B. S. Du, S. B. Hu*, J. Xiong*, D. Pan*.     Kolmogorov-Arnold Network Made Learning Physics Laws Simple. The     Journal of Physical Chemistry Letters. (2024)

●      J. Xiong,     J. C. He*, X. S. Leng*, T. Y. Zhang*. Gaussian process regressions on hot deformation     behaviors of FGH98 Nickel-based powder superalloy. Journal of     Materials Science & Technology. (2023)

●      R. Zhao,     J. C. He*, H. Tian, Y. J. Jing, J. Xiong*. Application of     Constitutive Models and Machine Learning Models to Predict the Elevated     Temperature Flow Behavior of TiAl Alloy. Materials (2023)

●      J. Xiong*, T. Y. Zhang*. Data-driven     glass-forming ability criterion for bulk amorphous metals with data     augmentation. Journal of Materials Science & Technology.     (2022)

●      J. Xiong^#, T. X. Lei^#,     D. M. Fu, J.W. Wu, T. Y. Zhang*. Data driven discovery of an analytic     formula for the life prediction of Lithium-ion batteries. Progress in     Natural Science: Materials International. (2022)

●      A. R. Wei, H. Ye, Z. B. Guo, J. Xiong*. SISSO-assisted prediction     and design of mechanical properties of porous graphene with a uniform     nanopore array. Nanoscale Advance. (2022)

●      J. Xiong,     S.Q. Shi*, T.Y. Zhang*. Machine learning of phases and mechanical     properties in complex concentrated alloys. Journal of Materials Science     & Technology. (2021)

●      J. Xiong,     S.Q. Shi*, T.Y. Zhang*. Machine learning of prediction of glass-forming     ability in bulk metallic glasses. Computational Materials Science.     (2021)

●      J. Xiong,     S. Q. Shi*, T. Y. Zhang*. A machine-learning approach to predicting and     understanding the properties of amorphous metallic alloys. Materials     & Design. (2020)

●      J. Xiong,     S. Q. Shi*, T. Y. Zhang*. Machine learning of mechanical properties of     steels. Science China Technological Science. (2020)

●      J. Xiong,     S. Q. Shi*, T. Y. Zhang*. Machine learning prediction of elastic properties     and glass-forming ability of bulk metallic glasses. MRS Communication.     (2019)

●      J. Xiong,     Q. Cai, Z. Q. Ma*, L. M. Yu, Y. C. Liu*.  Enhancement of Critical     Current Density in MgB2 Bulk with CNT-coated Al Addition. Journal of     Superconductivity and Novel Magnetism. (2014)