Correlating structure and material properties in soft materials

Abstract: This talk focuses on how statistics and machine learning can be used to correlate the nano- and microstructure of soft materials with their material properties. We work with different application areas where this is relevant, including packaging and barrier materials, hygiene products, pharmaceuticals, and food.

To develop models that map the relationship between a material’s structure and its functional properties, we combine:

1. methods for material imaging to understand what the structure looks like, ranging from indirect methods such as X-ray scattering to direct imaging in 2D and 3D, with or without a time component,
2. models for replicating and exploring 3D material structure using spatial statistics and generative AI,
3. numerical simulation of functional properties such as fluid and gas transport, and
4. statistical and machine learning models that connect structure to functional properties.

I will in this talk give an overview of some ongoing projects which are done in collaboration between RISE, the Department of Mathematical Sciences and Department of Physics at Chalmers, Chalmers Industriteknik, and industrial partners such as Tetra Pak, AstraZeneca, and Essity.

machine learningstatistics theory

Audience: researchers in the discipline

Gothenburg statistics seminar

Series comments: Gothenburg statistics seminar is open to the interested public, everybody is welcome. It usually takes place in MVL14 (http://maps.chalmers.se/#05137ad7-4d34-45e2-9d14-7f970517e2b60, see specific talk). Speakers are asked to prepare material for 35 minutes excluding questions from the audience.