Machine Learning-aided Multiscale Modelling Framework for Polymer Membranes

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 us 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.

The project achieved three significant goals:
1. We developed a ML-based multiscale simulation strategy, bridging atomic and coarse-grained scales, for the study of macromolecular and organic systems at bulk conditions.
2. We incorporated the developed ML method into open-source packages and widely used simulation tools.
3. We utilized the developed strategy to simulate organic liquids and polymers of industrial interest (polyethylene, PIM-1), to showcase its application to real-world test cases.
This work holds societal significance due to the pervasive use of polymers in manufacturing, healthcare, energy, and environmental technologies. Improving our capacity to design polymers efficiently has the potential to catalyse breakthroughs in diverse sectors. Further investigation will be required to address the new challenges related to the wide application of this novel methodology. Nonetheless, the proposed ML-based approach offers a pathway to potentially increase efficiency and versatility of molecular modelling, with broad implications for advancements in a multitude of industries and technologies.