Machine Learning Accelerate Development of New Sustainable Materials

Polymers and plastics play decisive roles in our everyday life and constitute a huge factor in our economy. There is a constant need for new production methods and new materials with new or improved properties; especially sustainability is of increasing importance.

Handling, processing, and learning from data is currently a major focus area. Data collection, data mining, and machine learning are driving new changes. In materials science, we foresee a data-driven approach to have a transforming effect on science, technology, and production. With data-driven techniques, it will become possible to engage data in more efficient and rational processes. We will use computational methods to design material and optimize the properties.






Want to contribute

  • Provide macroscopic parameters dataset from production line to train ML model
  • Collaboration on identification of macroscopic descriptors
  • Constant feedback on continuum mechanics modelling and chemical modifications
  • Other contribution?

Involved partners

Aarhus University

Theoretical Chemistry:

  • Ove Christiansen

Mechanics of Interfaces and Adhesion:

  • Michal Kazimierz Budzik
  • Ramin Aghababaei

Organic Surface Chemistry:

Biomechanics and Mechanobiology:

  • Jens Vinge Nygaard



If you’re interested in hearing more about this project, SPOMAN or want to hear about the possibilities for starting a collaboration, please contact us at