报告题目：Novel Advancements in Integrating Artificial Intelligence and Theoretical Models for Predicting Material Properties

报告人：Anh D. Phan （Phenikaa University）

报告时间：2025 年 8 月 15 日（周五） 15 : 00-16 : 30

报告地点：北洋园校区 49 教 410 室

报告摘要：

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, phase transition energy levels, bandgap, Curie temperatures, thermoelectric figure of merit (ZT), 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' (predicting material properties based on chemical composition), our developed models can be developed to perform 'inverse design' (suggesting chemical compositions to achieve desired material properties).

Host: K. Hansen, klavshansen@tju.edu.cn, 32-505

报告人简介：

RESEARCH ACTIVITIES
Machine learning and deep learning for materials science:
Structure-activity relationship, plasmonic nanostructures and amorphous materials, the glass transition, dynamic shear modulus, diffusion and relaxation in bulk and film systems of metallic glasses, polymers, amorphous drugs, colloids, thermal liquids, and organic materials under pressure effects, optical properties and photothermal heating of plasmonic nanostructures
Emerging topics: Theory of deep/machine learning/ simulations for molecular dynamics, diffusion of ionic liquids, polymers, and non-spherical molecule fluids.

EXPERIENCE
Phenikaa University, Vietnam; 2018-present, Faculty of Materials Science and Engineering
Kwansei Gakuin University 2019-2021
PhD in physics 2018, University of Illinois Urbana-Champaign, USA
MSc 2012, University of South Florida, USA
BsC 2009, Hanoi National University of Education

PUBLICATIONS
60+ peer-reviewed papers including 1 PRL, 1 in PNAS, and 1 in Nature Physics.
Citations = 1336 and h-index = 22