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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.

 

Aim

 

Workflow

Methods

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
Industry

 

 

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 stinek@inano.au.dk.