Predicting Properties of Materials With ML
Porous materials and their mass transport properties (for example fluids flowing in the pores, or diffusion of particles) are essential for the functionality of products in many industries e.g. pharma, hygiene, wound care, packaging, and energy. Understanding the microstructural geometry and how it relates to mass transport properties is crucial for designing better materials. We combine e.g. image analysis, statistics, numerical simulations, and machine learning to characterize materials and to predict, understand, and optimize their properties. In this talk, we will focus on the usefulness of machine learning in this area. We discuss a number of cases where the incorporation of machine learning techniques leads to valuable new tools and insights: for semantic segmentation of imaging data, accelerated analysis of data from other physical/chemical measurement techniques, and optimizing the design of materials structures.
Magnus Röding has an MSc in Engineering Physics and a PhD in Mathematical Statistics, both from Chalmers University of Technology. After a two-year stint at the University of South Australia, he joined Research Institutes of Sweden (RISE) in 2015 as a scientist and also leads the RISE Materials & AI area. Further, he is Adjunct Associate Professor in Mathematical Statistics at the Department of Mathematical Sciences, Chalmers, and collaborates on a daily basis both with academia and numerous companies. His expertise is in statistical modeling, image analysis, numerical and stochastic simulation techniques, and machine learning.