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. The long-term aim of my research is to make contributions to solving critical social challenges such as 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 2025 Wang, H., Cheng, J., Chen, H., Li, X., Liu, D., Li, X., . . . Zhang, J. (2025). Applications of perovskite oxides for oxygen evolution and oxygen reduction reactions in alkaline media. Energy Reviews, 4(2), 21 pages.
Scopus5 WoS22025 Jin, S., Li, X., Yang, G., Zhang, Z., Shi, J. Q., Liu, Y., & Zhao, C. -X. (2025). Active Learning-Based Prediction of Drug Combination Efficacy. ACS Nano, 19(18), 17929-17940.
2025 Tan, Z., Li, X., Bai, R., Guo, C., Han, X., Shi, J. Q., . . . Li, H. (2025). AI for Complex Catalytic Systems: High-Entropy Alloys in Electrocatalytic Acetylene Semihydrogenation. ACS Catalysis, 15(15), 13097-13106.
2024 Jin, S., Lan, Z., Yang, G., Li, X., Shi, J. Q., Liu, Y., & Zhao, C. (2024). Computationally guided design and synthesis of dual‐drug loaded polymeric nanoparticles for combination therapy. Aggregate, 5(5), e606-1-e606-10.
Scopus7 WoS72024 Li, H., Li, X., Wang, P., Zhang, Z., Davey, K., Shi, J. Q., & Qiao, S. -Z. (2024). Machine Learning Big Data Set Analysis Reveals C-C Electro-Coupling Mechanism. Journal of the American Chemical Society, 146(32), 22850-22858.
Scopus28 WoS27 Europe PMC42024 Tan, Z., Li, X., Zhao, Y., Zhang, Z., Shi, J. Q., & Li, H. (2024). Machine Learning‐Driven Selection of Two‐Dimensional Carbon‐Based Supports for Dual‐Atom Catalysts in CO2 Electroreduction. ChemCatChem, 16(22), e202400470-1-e202400470-8.
Scopus7 WoS72024 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.
Scopus50 WoS482023 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.
Scopus7 WoS72023 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.
Scopus13 WoS132023 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.
Scopus123 WoS116 Europe PMC412023 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.
Scopus8 WoS72023 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.
Scopus3 WoS4 Europe PMC12023 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), 87-1-87-11.
Scopus4 WoS32021 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.
Scopus25 WoS24 Europe PMC72021 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.
Scopus22 WoS22 Europe PMC52019 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.
Scopus23 WoS23 Europe PMC32017 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.
Scopus38 WoS352017 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.
Scopus27 WoS232017 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.
Scopus4 WoS42016 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.
Scopus46 WoS42 -
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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