TIAN TIAN, PhD
Pronouns: He/Him
Personal Website: https://scholar.google.com/citations?user=SwrDYScAAAAJ&hl=en
Contact
Assistant Professor, Faculty of Engineering - Chemical and Materials Engineering Dept
- tian.tian@ualberta.ca
- Phone
- (780) 492-1897
- Address
-
12-245 Donadeo Innovation Centre For Engineering
9211 116 StEdmonton ABT6G 2H5
Overview
Area of Study / Keywords
Machine Learning Multiscale Modeling Nanomaterials and Nanofabrication Surface and Interfacial Science Polymers Computational Materials Simulation
About
Dr. Tian Tian obtained his B.Sc. and M.Sc. in Chemistry from Tsinghua University. He completed his Ph.D. in Chemical Engineering at ETH Zürich under the supervision of Prof. Chih-Jen Shih. His doctoral research focused on the multiscale simulation and engineering of the interfacial properties of two-dimensional materials. From 2021 to 2023, he received the Swiss National Science Foundation (SNSF) Postdoc Mobility Fellowship to conduct postdoctoral research at Carnegie Mellon University with Prof. Zachary W. Ulissi. He worked on machine-learning-assisted material simulations, particularly the fine-tuning of pretrained graph neural network models for computational catalysis and developing machine-learning-assisted computational workflows. Before joining UofA, he briefly held a postdoctoral position at Georgia Institute of Technology under the supervision of Prof. Phanish Suryanarayana and Prof. Andrew J. Medford, developing software communication layers for the machine-learning-enabled density functional theory (DFT) package.
Research
Dr. Tian’s research group develops machine learning–accelerated simulation methods for the design of interfacial materials. The group explores applications in two-dimensional materials, energy storage systems, light-emitting polymers, and colloidal soft matter, addressing the challenge of vast configurational spaces that govern interfacial behavior. His work combines physics-based modeling and data-driven learning to accelerate multiscale simulations and enable predictive materials design. In parallel, the group advances open-source computational tools and machine-learning frameworks that bridge computation and experiment for optimizing material properties and synthesis processes.
Teaching
- CH E 318 Mass Transfer
- MAT E 664 Kinetics of Materials
Announcements
We are actively recruiting PhD and Master’s students for 2026! Interested students are encouraged to contact Dr. Tian for more information.
Courses
CH E 318 - Mass Transfer
Molecular and turbulent diffusion; mass transfer coefficients; mass transfer equipment design including absorption and cooling towers, adsorption and ion exchange. Prerequisites: CME 265, CH E 312 and CH E 343. Corequisite: CH E 314. Credit may not be obtained in this course if previous credit has been obtained for CH E 418.
Featured Publications
Tommaso Marcato, Jiwoo Oh, Zhan-Hong Lin, Tian Tian, Abhijit Gogoi, Sunil B. Shivarudraiah, Sudhir Kumar, Ananth Govind Rajan, Shuangshuang Zeng, Chih-Jen Shih
Nature Photonics. 2025 October; 10.1038/s41566-025-01785-z
Gianluca Vagli, Tian Tian, Franzisca Naef, Hiroaki Jinno, Kemal Celebi, Elton J. G. Santos, Chih-Jen Shih
Nature Communications. 2025 August; 10.1038/s41467-025-63074-1
Tian Tian, Lucas R Timmerman, Shashikant Kumar, Ben Comer, Andrew J Medford, Phanish Suryanarayana
Journal of Open Source Software. 2025 June; 10.21105/joss.07747
Shuangshuang Zeng, Tian Tian, Jiwoo Oh, Zhan-Hong Lin, Chih-Jen Shih
Nature Communications. 2025 April; 10.1038/s41467-025-58651-3
Joseph Musielewicz, Xiaoxiao Wang, Tian Tian, Zachary Ulissi
Machine Learning: Science and Technology. 2022 September; 10.1088/2632-2153/ac8fe0
Suiying Ye, Tian Tian, Andrew J. Christofferson, Sofia Erikson, Jakub Jagielski, Zhi Luo, Sudhir Kumar, Chih-Jen Shih, Jean-Christophe Leroux, Yinyin Bao
Science Advances. 2021 April; 10.1126/sciadv.abd1794
Tian Tian, Declan Scullion, Dale Hughes, Lu Hua Li, Chih-Jen Shih, Jonathan Coleman, Manish Chhowalla, Elton J. G. Santos
Nano Letters. 2020 February; 10.1021/acs.nanolett.9b02982
Tian Tian, Chander Shekhar Sharma, Navanshu Ahuja, Matija Varga, Raja Selvakumar, Yen‐Ting Lee, Yu‐Cheng Chiu, Chih‐Jen Shih
Small. 2018 December; 10.1002/smll.201804006
Lu Hua Li, Tian Tian, Qiran Cai, Chih-Jen Shih, Elton J. G. Santos
Nature Communications. 2018 March; 10.1038/s41467-018-03592-3
Tian Tian, Peter Rice, Elton J. G. Santos, Chih-Jen Shih
Nano Letters. 2016 August; 10.1021/acs.nanolett.6b01876
View additional publications
Research Students
Currently accepting undergraduate students for research project supervision.
Undergraduate students interested in research opportunities in machine learning and computational materials simulation projects are encouraged to email Dr. Tian with a brief statement of interest, a CV, and a recent transcript. Prior experience in coding for machine learning or data analysis (e.g., numPy, scikit-learn, pytorch) and/or computational chemistry (DFT, MD, Monte Carlo simulations) is beneficial but not required.