Coarse-grained molecular dynamics integrated with convolutional neural network for comparing shapes of temperature sensitive bottlebrushes

Abstract

Quantification of shape changes in nature-inspired soft material architectures of stimuli-sensitive polymers is critical for controlling their properties but is challenging due to their softness and flexibility. Here, we have computationally designed uniquely shaped bottlebrushes of a thermosensitive polymer, poly(N-isopropylacrylamide) (PNIPAM), by controlling the length of side chains along the backbone. Coarse-grained molecular dynamics simulations of solvated bottlebrushes were performed below and above the lower critical solution temperature of PNIPAM. Conventional analyses (free volume, asphericity, etc.) show that lengths of side chains and their immediate environments dictate the compactness and bending in these architectures. We further developed 100 unique convolutional neural network models that captured molecular-level features and generated a statistically significant quantification of the similarity between different shapes. Thus, our study provides insights into the shapes of complex architectures as well as a general method to analyze them. The shapes presented here may inspire the synthesis of new bottlebrushes.

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

“…Nonetheless, applications can be found also outside of the biomolecular domain, such as the work of Joshi et al that used CNN models to study the similarity between different conformations of solvated thermosensitive bottlebrush polymers in CG MD simulations below and above the lower critical solution temperature. Atomistic simulation trajectories have been also analyzed in terms of probability distribution of fragments, to extract molecular motifs , for the interpretation of the computed behavior in molecular systems and assist in sampling acceleration.…”

A review of recent advances and applications of machine learning in tribology

“…250 Known as coarse-graining, this approach has been instrumental in studying larger systems that require longer time scales and computational efficiency. 251,252 Traditionally, CG MD simulations have been used to study much larger tribological systems like polymer architectures, self-assembled particles, etc. [252][253][254] A study by Albina et al investigated the properties of lubricants enclosed between nanostructured metal (Fe) surfaces by obtaining a relationship between normal stress and the shear stress exerted on the lubricant molecules using CG MD simulations.…”

“…251,252 Traditionally, CG MD simulations have been used to study much larger tribological systems like polymer architectures, self-assembled particles, etc. [252][253][254] A study by Albina et al investigated the properties of lubricants enclosed between nanostructured metal (Fe) surfaces by obtaining a relationship between normal stress and the shear stress exerted on the lubricant molecules using CG MD simulations. The slope of shear stress vs. normal stress was used to calculate the CoF at different conditions.…”

“…A recent study in our group demonstrated a proof-of-concept study of CNN to map 3-dimensional structure of bottlebrush polymers and analyze shapes of complex structures. 331 CNNs, either in 1D, 2D, or 3D, which have shown significant potential to extract structural/imaging features to build their relationship with output in various research fields may prove valuable to assist these MD applications in tribology. [332][333][334][335][336] Researchers may find it challenging to train ML models at present since they may be unable to identify a combination of features that are appropriate for their learning objectives.…”

Bionic Soft Robot Review

“…The octopus uses them to grab, hold and attach to the surface of targets and eat food. [4]. The two features that both help this creature grip objects and are used in soft robots to improve grabbing skills.…”