HI vs AI: designing solvent-free brush networks with tissue-like mechanical properties

The ability to synthesize elastomeric and tissue-mimetic materials with programmable mechanical properties, enabling robust performance in constantly evolving conditions, is essential for future progress in soft materials design. We developed a design strategy of solvent-free networks made by crosslinking brush-like strands which is based on a theoretical model of the nonlinear network deformation and Machine Learning approach. Implementing a multi-layer feedforward artificial neural network (ANN), we take advantage of model-predicted structure-property cross-correlations between system code parameters A_i(l,v,b,ρ) and B_j(n_sc,n_g,n_x), describing system-specific chemistry and network strand architecture, and equilibrium mechanical properties of networks defined by the structural shear modulus G and firmness β. The ANN was trained by minimizing the mean-square error with Bayesian regularization to avoid overfitting for a data set of 118 experimental stress-deformation curves of networks with brush-like strands of PBA and PDMS having structural modulus G < 90 kPa and 0.01 ≤ β ≤ 0.9. The trained ANN was capable of predicting network mechanical properties based on strand chemistry and architecture with better than 95% confidence.