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What Can Machine Learning Help with Materials Modelling
Release time:April 3, 2023

Topic: What Can Machine Learning Help with Materials Modelling

Speaker:Prof. Dr. Bai-Xiang Xu, Technische Universität Darmstadt                          

Time: Thursday, April 6, 2023, 9:00 a.m.

Venue: C708, New Main Building, Xueyuan Road Campus


Mechanical and functional properties of engineering solid materials rely essentially on their microstructure, which further depend on the process history of the materials. It is generally a challenging task of materials modelling to recapture these correlations. The classical physics-law driven measures, e.g., constitutive modelling and multiscale techniques like homogenization, have been the focus of materials modelling in the last centuries. They have extended and will remain to push the research front of materials modelling greatly. However, due to the complexity of the microstructure and the advance of manufacturing, materials modelling and the related optimization remain a challenging task, which goes far beyond the limit of the current methodologies alone. It has to be assisted by other methodologies. With the advent of novel data science methods and Machine Learning (ML) approaches, which are particularly promising to recapture intricate correlations and for data processing. In combination of the classical and modern measures, there are new ground-breaking opportunities for materials modelling and design. In this work I will discuss the chances, capabilities and issues of Machine Learning and data science approach to assist materials modelling and simulations. After introducing Machine Learning in a nutshell and recap of materials modelling, I will demonstrate through case studies, how ML can be used in constitutive modelling, computational mechanics, and multiscale simulations, as well as microstructure characterization and reconstruction. Moreover, I will also show a ML surrogate model, trained on large thermal simulation data, for predication of melt pool size of powder bed fusion additive manufacturing from all kinds of materials and process parameters.

About the Speaker:

Professor Bai-Xiang Xu received the Bachelor's degree from Hohai University in 2002 and Ph.D. degree from Peking University in 2008, where she also received the Humboldt Research Fellowship in the same year. In 2016, she became a lifetime professor at the Technische Universität Darmstadt in Germany. Her recent research focuses on theoretical modeling and numerical simulation of multiphysics problems in functional and energy materials, as well as data-driven multiscale simulations and machine learning. In the past five years, she has published more than 100 SCI-indexed papers in prestigious international journals such as Science, Nature Materials, Materials Horizons, Nano Energy, JMPS, Acta Materialia, Int. J. Plasticity, and IJSS. She has also led or participated in about 15 European, German, and continental government research projects in the past five years.

School of Aeronautic Science and Engineering