Coarse-Grained Artificial Intelligence for Design of Brush Networks.
Andrey V DobryninAnastasia StroujkovaMohammad Vatankhah-VarnoosfaderaniSergei S SheikoPublished in: ACS macro letters (2023)
The ability to synthesize elastomeric materials with programmable mechanical properties is vital for advanced soft matter applications. Due to the inherent complexity of hierarchical structure-property correlations in brush-like polymer networks, the application of conventional theory-based, so-called Human Intelligence (HI) approaches becomes increasingly difficult. Herein we developed a design strategy based on synergistic combination of HI and AI tools which allows precise encoding of mechanical properties with three architectural parameters: degrees of polymerization (DP) of network strands, n x , side chains, n sc , backbone spacers between side chains, n g . Implementing a multilayer feedforward artificial neural network (ANN), we took advantage of model-predicted structure-property cross-correlations between coarse-grained system code including chemistry specific characteristics S = [ l , v , b ] defined by monomer projection length l and excluded volume v , Kuhn length b of bare backbone and side chains, and architecture A = [ n sc , n g , n x ] of polymer networks and their equilibrium mechanical properties P = [ G , β] including the structural shear modulus G and firmness parameter β. The ANN was trained by minimizing the mean-square error with Bayesian regularization to avoid overfitting using a data set of experimental stress-deformation curves of networks with brush-like strands of poly( n -butyl acrylate), poly(isobutylene), and poly(dimethylsiloxane) having structural modulus G < 50 kPa and 0.01 ≤ β ≤ 0.3. The trained ANN predicts network mechanical properties with 95% confidence. The developed ANN was implemented for synthesis of model networks with identical mechanical properties but different chemistries of network strands.