Materials science explores the properties of materials in a variety of ways: from theoretical models, to computational models and real-world experiments. These analyses allow us to devise new materials that suit specific needs. The ML-MULTIMEM project focuses on empowering the molecular simulation of polymers - a ubiquitous family of materials in manufacturing, healthcare, energy, and environmental technologies - with artificial intelligence and especially machine learning methods. This synergy allows us to model complex materials at different scales - what is termed "multi-scale modeling" - with an innovative approach. This offers the opportunity to address a critical challenge in materials science: the bottom-up rational design of complex polymers for diverse applications, using molecular simulation methods. These materials require multiscale strategies to be studied, typically including coarse grained representations. To overcome limitations associated with traditional coarse graining strategies, this project integrated Machine Learning (ML) into molecular simulation methods, utilizing Graph Convolutional Neural Networks to obtain coarse grained force fields for molecular simulations. 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.
The project is a collaboration between the Software and Knowledge Engineering Lab (SKEL) of the Institute of Informatics and Telecommunications and the Molecular Thermodynamics and Modelling of Materials Laboratory (MTMML) of the Institute of Nanoscience and Nanotecnology of NCSR “Demokritos”, Athens, Greece. The project is funded under the Marie Skłodowska-Curie Actions programme (H2020-MSCA-IF-2020, Grant ID:101030668).