SOURCE-LINKED INTELLIGENCE

Machine-learning polymer Gel's ELasticity and Structure

CORDIS · observation · Publication date unknown

lations and experiments must be performed. To overcome this difficulty, in the present proposal MGELS -Machine-learning polymer Gel’s ELasticity and Structure- I will exploit my existing knowledge on Machine Learning (ML) methods to develop novel tools able to predict structural and elastic properties of hydrogels and microgel particles to design in silico polymer networks with desired features. The project is based on four objectives. The first is to develop a ML-Neural Network (ML-NN) able to predict the network structure using data from molecular dynamics simulations of microgels and hydrogels already collected from the supervisor. The second is to explore structural and elastic properties of new configurations of networks, following the methods established in the host group. As a third objective, we will create a new database of polymer networks that will be made open access at the end of the project. Finally, we will extend the ML-NN approach to predict elastic properties and to identify the onset of auxetic behavior. With the accomplishment of these goals, we will be able to fu

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recordType

award

status

SIGNED

region

EU

value

172750.08

unit

EUR