Dr. Chen Dong, a 2022 Ph.D. graduate from Professor Pan Feng’s team at Peking University Shenzhen Graduate School, has been awarded the 2023 Graduate Thesis Award (GTA) Silver Prize by the World Federation of Chinese Mathematicians (ICCM). This prestigious award recognises the exceptional contributions of doctoral students in cutting-edge mathematical research that demonstrates creativity, originality, and depth. Dr. Chen earned the honour for his doctoral thesis titled The Development and Application of Machine Learning and Mathematical Methods for Materials Science.
Chen Dong Receives the ICCM Best Ph.D. Thesis Award Certificate
The award committee praised Dr. Chen's work for exploring the integration of machine learning with mathematical models to accelerate the discovery and design of materials. The committee noted that the thesis focuses on optimising material performance, reducing costs, and enhancing computational methods in materials science, ultimately improving research efficiency. This recognition highlights the innovative work of both Dr. Chen and Professor Pan Feng's team in interdisciplinary research at the intersection of mathematics and materials science.
Application of Fundamental Mathematics of Directed Graphs in Molecular and Materials Science
Dr. Chen joined Professor Pan Feng’s lab in 2017, where he worked on graph-theory-based structural chemistry and materials genomics. Later, he was co-supervised by Professor Guo-Wei Wei at Michigan State University’s Department of Mathematics, where he expanded his research into the intersection of graph theory, topology, and materials science. Dr. Chen developed innovative methodologies that combine mathematics, structural chemistry, and artificial intelligence (AI) to address critical challenges in materials research.
Dr. Chen’s key achievements include: (1) developing a Transformer deep learning model enhanced by algebraic graph theory, which significantly improved the accuracy of small molecule property predictions (Nat. Comm. 2021, 3521); (2) proposing a high-throughput screening strategy based on algebraic topology and machine learning, which successfully identified new potential lithium-ion conductor materials (JACS, 2025, revised); and (3) introducing a new approach to analyzing multi-body interactions using spectral graph theory (Foundations of Data Science 2023, 558), providing powerful tools for predicting material stability.
In collaboration with the Mathematical Research Centre of Professor Shing-Tung Yau, a Fields Medalist (1982), Dr. Chen successfully applied directed graph theory to molecular and materials science, a pioneering achievement in the field (JPCL, 2023, 954). This research has led to methods that accelerate the discovery of new materials and support the development of "AI for Science."
