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Machine Learning-aided Multiscale Modelling Framework for Polymer Membranes

Project description

Towards more efficient polymer membrane design

Funded by the Marie Skłodowska-Curie Actions programme, the ML-MULTIMEM project targets the integration of AI algorithms in multiscale molecular simulation methodologies for polymers, to advance innovation in polymer-based membranes for greenhouse gas emissions reduction. A hierarchical simulation strategy will be developed to efficiently model materials' properties at multiple scales: atomic, mesoscopic and macroscopic. The machine learning-aided modelling approach will be used to systematically extract accurate coarse-grained representations and force fields for polymer systems, extending the applicability and generalisation of these novel molecular simulation techniques to a range of complex chemical systems important for several critical applications. Molecular simulations will be also coupled with continuum models for the development of a general multicomponent predictive framework.

Objective

The goal of this project is to build a systematic modelling framework for advanced polymer materials, that are widely employed in numerous membrane separation applications, especially as gas separation media for carbon capture. Polymers are very challenging to simulate, due to the wide range of timescales that are present in these systems and require elaborate system-specific multiscale strategies. A hierarchical simulation strategy will be developed, encompassing atomistic, mesoscopic and continuum scales, integrating machine learning techniques. The artificial intelligence aided multi-scale approach proposed constitutes a generalized methodology for the efficient computational study of polymers. The synergy of unsupervised machine learning (ML) clustering techniques and neural networks (NN), will enable the extraction of accurate coarse-grained (CG) representations and force fields of the polymer systems, bringing this complex problem within computational reach. Optimized ML models will be integrated into Molecular Dynamics and innovative Monte Carlo simulations at the CG level, with the latter enabling the equilibration up to high molecular weight of polymers of complex chemical constitution, and the prediction of their micro- and macroscopic behaviour. Molecular simulation results will be integrated into macroscopic equation-of-state-based models, resulting in a bottom-up determination of the relevant process parameters for membrane separations (permeability and selectivity) in a wide range of conditions, for pure gases and gas mixtures. Systematic hierarchical modelling provides unique property prediction means, simultaneously shedding light on the mechanisms that are responsible for the materials end-use performance. This is a stepping stone towards the rational design of advanced processes from the molecular level all the way up to industrial applications, which in the present case involve novel separation technologies with great environmental impact.

Coordinator

"NATIONAL CENTER FOR SCIENTIFIC RESEARCH ""DEMOKRITOS"""
Net EU contribution
€ 153 085,44
Address
END OF PATRIARCHOU GRIGORIOU E AND 27 NEAPOLEOS STREET
15341 Agia Paraskevi
Greece

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Αττική Aττική Βόρειος Τομέας Αθηνών
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Research Organisations
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Total cost
€ 153 085,44