STAFF PROFILE
Dr Tu Le
Position:
Senior Lecturer, Mechatronics
College / Portfolio:
STEM College
School / Department:
STEM|School of Engineering
Phone:
+61399252216
Email:
tu.le@rmit.edu.au
Campus:
City Campus
Contact me about:
Research supervision
- Huynh, H.,Kelly, T.,Vu, L.,Hoang, T.,Nguyen, P.,Le, T.,Jarvis, E.,Phan, H. (2023). Quantum Chemistry-Machine Learning Approach for Predicting Properties of Lewis Acid-Lewis Base Adducts In: ACS Omega, 8, 19119 - 19127
- Zhao, Y.,Houshyar, S.,Le, T. (2023). A review on the application of molecular descriptors and machine learning in polymer design In: Polymer Chemistry, 14, 3325 - 3346
- Mai, H.,Li, X.,Lu, J.,Wen, x.,Le, T.,Russo, S.,Chen, D.,Caruso, R. (2023). Synthesis of Layered Lead-Free Perovskite Nanocrystals with Precise Size and Shape Control and Their Photocatalytic Activity In: Journal of the American Chemical Society, 145, 17337 - 17350
- Irfan, M.,Zuraqi, K.,Nguyen, K.,Le, T.,Jabbar, F.,Ameen, M.,Parker, C.,Chiang, K.,Jones, L.,Elbourne, A.,McConville, C.,Yang, D.,Daeneke, T. (2023). Liquid metal-based catalysts for the electroreduction of carbon dioxide into solid carbon In: Journal of Materials Chemistry A, 11, 14990 - 14996
- Rahman, M.,Dip, T.,Haase, T.,Truong, Y.,Le, T.,Houshyar, S. (2023). Fabrication of Zein-Based Fibrous Scaffolds for Biomedical Applications—A Review In: Macromolecular Materials and Engineering, 308, 1 - 23
- Orhan, I.,Le, T.,Babarao, R.,Thornton, A. (2023). Accelerating the prediction of CO2 capture at low partial pressures in metal-organic frameworks using new machine learning descriptors In: Communications Chemistry, 6, 1 - 12
- Wu, C.,Peng, C.,Le, T.,Das, R.,Tran, P. (2023). Tunable 3D printed composite metamaterials with negative stiffness In: Smart Materials and Structures, 32, 1 - 16
- Li, X.,Mai, H.,Lu, J.,Wen, x.,Le, T.,Russo, S.,Winkler, D.,Chen, D.,Caruso, R. (2023). Rational Atom Substitution to Obtain Efficient, Lead-Free Photocatalytic Perovskites Assisted by Machine Learning and DFT Calculations In: Angewandte Chemie - International Edition, 62, 1 - 12
- Williams-Noonan, B.,Speer, M.,Le, T.,Sadek, M.,Thompson, P.,Norton, R.,Yuriev, E.,Barlow, N.,Chalmers, D.,Yarovsky, I. (2022). Membrane Permeating Macrocycles: Design Guidelines from Machine Learning In: Journal of Chemical Information and Modeling, 62, 4605 - 4619
- Mai, H.,Le, T.,Chen, D.,Winkler, D.,Caruso, R. (2022). Machine Learning in the Development of Adsorbents for Clean Energy Application and Greenhouse Gas Capture In: Advanced Science, 9, 1 - 22
Materials science, machine learning, artificial neural networks, computational chemistry,
2 PhD Completions and 1 Masters by Research Completions7 PhD Current Supervisions and 1 Masters by Research Current Supervisions
- Data-driven development of photocatalytic and optoelectronic perovskites. Funded by: ARC Discovery Projects commencing in 2022 from (2022 to 2025)
- Autonomous platform for remote aerosol-based threat neutralisation and soldier countermeasure therapeutics. Funded by: Defence Science Institute (DSI) Grant for Scholarships from (2018 to 2021)