Integrating Machine Learning in the Coarse-Grained Molecular Simulation of Polymers

Abstract

Machine learning (ML) is having an increasing impact on the physical sciences, engineering, and technology and its integration into molecular simulation frameworks holds great potential to expand their scope of applicability to complex materials and facilitate fundamental knowledge and reliable property predictions, contributing to the development of efficient materials design routes. The application of ML in materials informatics in general, and polymer informatics in particular, has led to interesting results, however great untapped potential lies in the integration of ML techniques into the multiscale molecular simulation methods for the study of macromolecular systems, specifically in the context of Coarse Grained (CG) simulations. In this Perspective, we aim at presenting the pioneering recent research efforts in this direction and discussing how these new ML-based techniques can contribute to critical aspects of the development of multiscale molecular simulation methods for bulk complex chemical systems, especially polymers. Prerequisites for the implementation of such ML-integrated methods and open challenges that need to be met toward the development of general systematic ML-based coarse graining schemes for polymers are discussed.

Structural Coarse-Graining via Multiobjective Optimization with Differentiable Simulation

“…Recent breakthroughs in machine-learning force fields (MLFF) hold great promise as a viable approach for developing accurate and transferable CG models. , In particular, MLFFs have shown their effectiveness in CG simulations of various systems, including organic molecules, liquids, , and fast-folding proteins . However, the extent to which MLFFs can be applied to condensed-phase macromolecular systems remains an intriguing question for further exploration. ,, Furthermore, an ideal scenario for the development of CG models envisions integration with an automated optimization loop, possibly through active learning schemes, as done in MLFFs of interatomic potential. , This integration would unlock the full potential of coarse-graining techniques, enabling the modeling of complex systems across extensive spatiotemporal scales. DiffCG is anticipated to be a transformative tool in structural coarse-graining, with the potential to address a wide range of challenges and pave the way for innovative developments in CG modeling.…”

“…38 However, the extent to which MLFFs can be applied to condensed-phase macromolecular systems remains an intriguing question for further exploration. 1,39,84 Furthermore, an ideal scenario for the development of CG models envisions integration with an automated optimization loop, possibly through active learning schemes, as done in MLFFs of interatomic potential. 85,86 This integration would unlock the full potential of coarse-graining techniques, enabling the modeling of complex systems across extensive spatiotemporal scales.…”

“…Recently, machine learning has emerged as a unique and powerful tool for the development of bottom-up coarse-graining methods. − It has shown remarkable potential in improving essential components of coarse-graining methods, including determining optimal coarse-grained mapping schemes, , facilitating back-mapping processes, − refining coarse-grained force fields, and optimizing associated parameters. ,, Moreover, an increasingly noteworthy technique garnering attention is differentiable simulations (DiffSim). DiffSim built on automatic differentiation has been utilized to accelerate quantum calculations and generate protein structures and computational fluid dynamics.…”

Coarse-Grained Molecular Dynamics Modeling of Polyvinyl Chloride: Solvent Interactions, Mechanical Behavior, and Dehydrochlorination Effects

“…Molecular dynamics (MD) simulations can potentially help guide the design of upcycling strategies for processing PVC waste. The growth in computational power and the development of improved simulation algorithms have greatly increased the quantitative performance of polymer simulation studies. , Despite these advances, the inherent complexity of polymer systems often requires the application of multiscale simulation methodologies, ranging from quantum chemistry to atomistic and mesoscopic methods up to continuum modeling, in order to capture the broad range of length and time scales at which relevant phenomena emerge. , Coarse graining (CG) is an especially valuable technique for modeling polymer systems . A CG molecular description facilitates the extension of molecular modeling to mesoscopic length and time scales, although at the cost of a loss in atomistic detail.…”

“…14,15 Despite these advances, the inherent complexity of polymer systems often requires the application of multiscale simulation methodologies, ranging from quantum chemistry to atomistic and mesoscopic methods up to continuum modeling, in order to capture the broad range of length and time scales at which relevant phenomena emerge. 14,16 Coarse graining (CG) is an especially valuable technique for modeling polymer systems. 17 A CG molecular description facilitates the extension of molecular modeling to mesoscopic length and time scales, although at the cost of a loss in atomistic detail.…”