Title: Novel Advancements in Integrating Artificial Intelligence and Theoretical Models for Predicting Material Properties
Speaker: Dr.Anh D. Phan
Time: 2026-03-03 16：00
Venue: Room W105, Physics Building
Abstract: In this study, we integrate machine learning/deep learning models into theoretical and simulation frameworks to predict and analyze optical, thermal, magnetic, and molecular dynamics properties in metallic glasses, oxides, polymers, phosphors, perovskites, and thermoelectric materials. Machine learning models are constructed to predict the melting temperatures, glass transition temperatures (Tg), emission peak positions, and other properties from their chemical compositions. Our approach, despite its simplicity, provides predictions with higher accuracy compared to prior research. This approach proves particularly beneficial for predicting properties of novel materials not yet synthesized. The predicted-Tg values from simulations and AI are integrated into the Elastically Cooperative Nonlinear Langevin Equation theory to determine the temperature dependence of structural relaxation time of amorphous materials. All our calculations show good agreement with experimental data and prior simulations without any adjustable parameters. Beyond 'forward prediction' (prediciting material properties based on chemical composition), our developed models can be developed to perform 'inverse design' (suggesting chemical compositions to achieve desired material properties).
Bio: Dr. Anh D. Phan graduated from the Talented Bachelor Program at Hanoi National University of Education in 2009. After more than a year working at the Institute of Physics, Vietnam Academy of Science and Technology, he began his graduate studies in the United States in 2011. He earned a Master’s degree in Applied Physics from the University of South Florida in 2013 and received his Ph.D. in Physics from the University of Illinois at Urbana-Champaign in 2018 (December 24th). After completing his doctorate, Dr. Phan joined Phenikaa University in November 2018. From 2019 to 2021, he was awarded the prestigious JSPS Postdoctoral Fellowship for Research in Japan. Dr. Phan’s research interests span a broad range of topics from plasmonic nanostructures and metamaterials to glassy dynamics in polymers and amorphous materials. His work integrates theoretical modeling with simulations and machine learning approaches. Dr. Phan has published over 80 scientific papers in high-impact ISI-indexed journals including Macromolecules, Physical Review Letters, PNAS, and Nature Physics. In 2020, he received the Young Researcher Award from the Vietnam Theoretical Physics Society. In 2025, he received the DCMP Young Scientist Award (Silver Medal) from the Division of Condensed Matter Physics (DCMP) of the Association of Asia Pacific Physical Societies (AAPPS). Since December 2025, Dr. Phan has joined VinUniversity, where he pursues interdisciplinary research at the interface of physics, computation, and data-driven materials science. He also serves on the editorial board of the Journal of Science: Advanced Materials and Devices (JSAMD), which currently has an impact factor of 6.7.
