In this webinar, presenters demonstrate how to use AFM data to apply machine learning to materials development, also introducing AFM modes used in automated polymer R&D.
For material development, it is of interest to understand the relationship between microstructure and properties at the macroscale. In this webinar, the presenters highlight how AFM-based nanomechanical measurements and machine learning can provide new insights into the mechanical properties of polymeric materials.
The first part of this webinar is dedicated to the following goals:
In the second part, the presenters discuss computational methods and ML algorithms dealing with data clustering (such as K-Means or Automatic Gaussian Mixture Model) that can be used to detect the different domains and (inter)phases in materials (e.g. polymer blends, hydrogels, nanocomposites, block copolymers, etc.) by partitioning the recorded data into clusters according to similarities. Additionally, based on the Tabor coefficient calculation, the presenters also propose some protocols that can be easily implemented to rapidly determine which mechanical model(s) can be applied to obtain quantitative mapping of the mechanical properties for each local domain or phase.
This algorithmically driven approach enables AFM users to analyze materials with more complex architectures and/or other properties, opening new avenues of research for advanced materials with specific functions and desired properties leading to the creation of functional and more reliable structural materials.
This webinar was presented on March 16, 2021.
Find out more about the technology featured in this webinar or our other solutions for AFM:
Bede Pittenger, Ph.D., Sr. Staff Development Scientist, AFM Applications, Bruker Nano Surfaces
Prof. Dr. Philippe LECLERE
Associate Professor, University of Mons