MASS -1 W1 36.03 ! 1-site water bead (H2O) MASS -1 D1 40.062 ! 1-site heavy water bead (D2O) MASS -1 BZ 26.038 ! Benzene C2H2 bead MASS -1 C2E 28.054 ! 2:1 mapped alkane end bead MASS -1 C2M 27.046 ! 2:1 mapped alkane inner bead MASS -1 C3E 43.10 ! 3:1 mapped alkane end bead MASS -1 C3M 42.09 ! 3:1 mapped alkane inner bead MASS -1 AM 43.024 ! DMF polar bead MASS -1 CGD2 30.068 ! DMF non-polar bead MASS -1 AMP 43.0254 ! NIPAM hydrophilic bead MASS -1 ISP 43.080 ! NIPAM isopropyl bead MASS -1 COH 45.016 ! carboxylic acid group beadRESI HOH 0.0 ! water GROUP ATOM W W1 0.0 RESI DOD 0.0 ! heavy water GROUP ATOM D D1 0.0 RESI BZN 0.0 ! benzene GROUP ATOM C1 BZ 0.0 ! -C1-ATOM C2 BZ 0.0 ! / \ ATOM C3 BZ 0.0 ! C2------C3 BOND C1 C2 C2 C3 C1 C3 RESI HEX 0.0 ! hexane with 2:1 mapping. Other alkanes can be modelled similarly GROUP ATOM C1 C2E 0.0 ! ATOM C2 C2M 0.0 ! C1-C2-C3 ATOM C3 C2E 0.0 !

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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.

This review summarises recent advances in the use of machine learning for predicting friction and wear in tribological systems, material discovery, lubricant design and composite formulation. Potential future applications and areas for further research are also discussed.

No abstract