Dr. Yiqing Chen received her Ph.D. in Materials Engineering from McGill University. She was previously a postdoctoral fellow at Northwestern University and the University of Toronto. With a background in materials science, electrochemistry, and machine learning applications, her research focuses on understanding the physical and chemical mechanisms underlying electrocatalysis and advancing AI-driven catalyst design.
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Eric and Wendy Schmidt AI in Science Postdoctoral Fellowship
Email: yiqingchen@nus.edu.sg

Dr. Shangwen Fang earned his Ph.D. in Chemistry from Nanjing University and his M.S. in Chemistry from Soochow University. With an academic background in organic synthesis, polymer chemistry, and DFT-based mechanistic studies, his currentresearch focuses on developing AI-driven retrosynthetic planning frameworks for solid-phase synthesis to enable fully automated small-molecule organic synthesis.
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Co-Advisor: A/P Jie Wu
Email: sw.fang@nus.edu.sg

Dr. Nihang Fu received her Ph.D. in Computer Science from the University of South Carolina and her M.S. in Electrical and Computer Engineering from Northeastern University. Her research expertise spans machine learning, deep learning, and their applications in materials science. She is currently focused on advancing AI for Science, with particular emphasis on crystal materials and their applications in catalysis.

Email: nihang.fu@nus.edu.sg

Dr. Lulu Wang holds a Ph.D. and M.S. in Chemistry from the National University of Singapore, and a B.S. in Chemistry from Soochow University. Her expertise spans on-surface synthesis, supramolecular catalysis, and the application of machine learning in catalyst development. Currently, her research focuses on leveraging machine learning techniques to predict highly efficient catalysts.

Email: wanglulu@nus.edu.sg

Dr. Chao Yang earned his M.S. and Ph.D. in Inorganic Chemistry from Fudan University. With a background in electrochemistry, materials science, and catalytic reactions, he is focusing on understanding catalyst stability under working conditions using machine learning methods.

Email: c_yang@nus.edu.sg

Yueyang Lin earned his M.Sc. in Chemistry from the National University of Singapore and B.Sc in Chemical Engineering from the East China University of Science and Technology. He is focusing on intersection of AI and Density Functional Theory (DFT), particularly in Oxygen Evolution Reaction (OER) and Double Atom Catalyst (DAC).

Email: e1353154@u.nus.edu

Yan Liu earned her M.S. in Chemistry from The University of Auckland with First-Class Honours, with a background in electrochemistry, theoretical catalysis, heterogeneous catalysis, and computational chemistry. She is focusing on understanding and designing catalysts for electrocatalytic reactions using Density Functional Theory (DFT) and ab initio molecular dynamics (AIMD) calculations.

Email: yan.liu@u.nus.edu

Jensie Low graduated with the Erasmus Mundus Masters of Science in Nanoscience and Nanotechnology from KU Leuven and Université Grenoble Alpes and her BSc in Science (Chemistry) from the National University of Singapore.

Email: jensielow@u.nus.edu

Yujie Luo earned his B.S. in Chemistry from Wuhan University, with a background in electrochemistry and organic systhesis. He is focusing on understanding and designing molecular catalysts using Density Functional Theory and machine learning methods.

Email: e1353438@u.nus.edu

Yiwen Yao earned her M.S. in Materials Science and Engineering from the National University of Singapore and B.E. in Polymer Materials and Engineering from Sun Yat-sen University. With a background in electrochemistry and polymer chemistry, she is currently focusing on high-throughput screening of high-entropy alloys and oxides for electrocatalysis reactions.

Email: e1142446@u.nus.edu

Zhiquan Zeng earned his M.S. in Powder Metallurgy Research Institute from Central South University, with a background in cathode materials, hydrogen storage materials, and water electrolysis catalysts. He is focusing on designing catalysts for water electrolysis based on Density Functional Theory (DFT) and machine learning methods.

Email: e1583458@u.nus.edu

Jinbo Zhu earned his B.S. in Theoretical Physics from the University of British Columbia, with a background in Computational Materials Science, Density Functional Theory (DFT), and Solid-State Physics. He is currently focusing on machine learning-driven predictions of catalytic structures and exploring their properties.
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Email: zhujinbo@u.nus.edu

Dr. Wentao Wang is a professor at Guizhou Education University. He received his Ph.D. degree in condensed matter physics from Xiangtan University and his postdoctoral degree from Henan University. His research interests focus on the density functional theory (aided by machine learning) to study energy storage and conversion materials and semiconductor defect physics.
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Email: chmv595@visitor.nus.edu.sg