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, for accelerated analysis of data from other physical/chemical measurement techniques, and for optimizing the design of materials structures.