Dr Henry Li
Research Associate
Australian Institute for Machine Learning - Projects
Faculty of Sciences, Engineering and Technology
Eligible to supervise Masters and PhD (as Co-Supervisor) - email supervisor to discuss availability.
I am a Postdoctoral Research Fellow at the Australian Institute for Machine Learning, The University of Adelaide, collaborating with Prof. Javen Shi. I work in the interdisciplinary area of machine learning and computational chemistry and have gained valuable experience in utilizing them for the purpose of renewable-energy materials discovery and drug discovery. These efforts play a crucial role in addressing pressing social challenges like climate change and incurable diseases. I have published over 10 peer-reviewed papers in prestigious journals, including Nano Letters, Nano Energy, JCTC, JPCL, and JMCA.
In addition to publications, I have also won several international machine learning competitions, including the Winner of the 2023 Open Catalyst Challenge and the 2nd prize in the Tencent Alchemy Contest. I also gained industry experience as an algorithm intern at Galixir Technology.
I am continuously seeking PhD students passionate about AI4Science direction, particularly in areas such as material discovery or drug discovery. I welcome applicants from non-CS backgrounds, such as physics, chemistry, biology, and materials science.
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Education
Date Institution name Country Title University of Newcastle Australia Australia PhD Huazhong University of Science and Technology China Master of Engineering Mines ParisTech France Master of Science -
Research Interests
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Journals
Year Citation 2024 Lin, L., Jones, T. W., Yang, T. C. J., Li, X., Wu, C., Xiao, Z., . . . Wang, X. (2024). Hydrogen bonding in perovskite solar cells. Matter, 7(1), 38-58.
Scopus3 WoS22024 Li, X., Yu, Y., Zhang, R., & Guo, W. (2024). Cobalt etched graphite felt electrode for enhanced removal of organic pollutant in aqueous solution with a solid polymer electrolyte. Environmental Science and Pollution Research, 31(12), 18614-18624.
2023 Zhao, Y., Li, H., Shan, J., Zhang, Z., Li, X., Shi, J. Q., . . . Li, H. (2023). Machine Learning Confirms the Formation Mechanism of a Single-Atom Catalyst via Infrared Spectroscopic Analysis. Journal of Physical Chemistry Letters, 14(49), 11058-11062.
2023 Li, X., Chiong, R., Hu, Z., & Page, A. J. (2023). A graph neural network model with local environment pooling for predicting adsorption energies. Computational and Theoretical Chemistry, 1226, 1-8.
2023 Li, H., Jiang, Y., Li, X., Davey, K., Zheng, Y., Jiao, Y., & Qiao, S. -Z. (2023). C₂₊ Selectivity for CO₂ Electroreduction on Oxidized Cu-Based Catalysts. Journal of the American Chemical Society, 145(26), 14335-14344.
Scopus25 WoS4 Europe PMC42023 Li, X., Li, H., Zhang, Z., Shi, J. Q., Jiao, Y., & Qiao, S. -Z. (2023). Active-learning accelerated computational screening of A₂B@NG catalysts for CO₂ electrochemical reduction. Nano Energy, 115, 108695-1-108695-9.
Scopus22023 Li, X., Shi, J. Q., & Page, A. J. (2023). Discovery of Graphene Growth Alloy Catalysts Using High-Throughput Machine Learning. Nano Letters, 23(21), 9796-9802.
2023 Yuwono, J. A., Li, X., Doležal, T. D., Samin, A. J., Shi, J. Q., Li, Z., & Birbilis, N. (2023). A computational approach for mapping electrochemical activity of multi-principal element alloys. npj Materials Degradation, 7(1), 11 pages.
2021 Li, X., Chiong, R., & Page, A. J. (2021). Group and Period-Based Representations for Improved Machine Learning Prediction of Heterogeneous Alloy Catalysts. The journal of physical chemistry letters, 12(21), 5156-5162.
Scopus19 WoS14 Europe PMC52021 Li, X., Chiong, R., Hu, Z., & Page, A. J. (2021). Low-Cost Pt Alloys for Heterogeneous Catalysis Predicted by Density Functional Theory and Active Learning. The journal of physical chemistry letters, 12(30), 7305-7311.
Scopus10 WoS7 Europe PMC12019 Li, X., Chiong, R., Hu, Z., Cornforth, D., & Page, A. J. (2019). Improved Representations of Heterogeneous Carbon Reforming Catalysis Using Machine Learning. Journal of Chemical Theory and Computation, 15(12), 6882-1-6894-13.
Scopus18 WoS15 Europe PMC22017 Li, X., Li, Z., Yang, X., Jia, L., Fu, Y. Q., Chi, B., . . . Li, J. (2017). First-principles study of the initial oxygen reduction reaction on stoichiometric and reduced CeO<sub>2</sub> (111) surfaces as a cathode catalyst for lithium–oxygen batteries. Journal of Materials Chemistry A, 5(7), 3320-3329.
Scopus34 WoS312017 Jiang, Y., Cheng, J., Zou, L., Li, X., Huang, Y., Jia, L., . . . Li, J. (2017). Graphene Foam Decorated with Ceria Microspheres as a Flexible Cathode for Foldable Lithium-Air Batteries. ChemCatChem, 9(22), 4231-4237.
Scopus22 WoS192017 Yang, X., Li, Z., Li, X., Wang, A., Jia, L., Chi, B., . . . Li, J. (2017). Oxygen reduction on zirconium-stabilized-PdO surfaces: a first-principles study. RSC Advances, 7(65), 41057-41062.
Scopus3 WoS32016 Jiang, Y., Cheng, J., Zou, L., Li, X., Gong, Y., Chi, B., . . . Li, J. (2016). In-Situ Growth of CeO2 Nanoparticles on N-doped Reduced Graphene Oxide for Anchoring Li2O2 Formation in Lithium-Oxygen Batteries. Electrochimica Acta, 210, 712-719.
Scopus42 WoS35 -
Preprint
Year Citation 2024 Qiao, S., Li, H., Li, X., wang, P., zhang, Z., Davey, K., & Shi, J. (2024). Establishing C-C electro-coupling mechanism via big data analyses of reaction networks.
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Current Higher Degree by Research Supervision (University of Adelaide)
Date Role Research Topic Program Degree Type Student Load Student Name 2023 Co-Supervisor Data-driven Machine-learning Assisted Design of Electrocatalysts for Green C2 Chemical Production Doctor of Philosophy Doctorate Full Time Mr Zhen Tan
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