From RVE Data to Auxetic Design Rules: Interpretable Feature Analysis and Machine Learning-Based Modeling of Microstructured Materials

We study 2D RVEs based on microstructures inspired by limpet teeth with the objective of efficiently identifying auxetic designs and building surrogates for effective elastic response. The starting point is an unbalanced database; thus, we run a weighted random forest classifier and a neural network classifier to balance it. The resulting feature importances provide an interpretable ranking of 18 geometric and material variables and guide importance-biased Monte Carlo sampling. Random forest and FCNN classifiers are used to prioritize candidates. Dataset rebalancing is achieved by adding newly FEM-confirmed auxetic samples and applying clustering-guided downsampling to the non-auxetic majority. On this final set, a multi-output FCNN regressor predicts nine targets: inclusion volume fractions and minima/means/maxima of Young’s modulus and Poisson’s ratio. Overall, the framework supports rapid, interpretable screening and property prediction for auxetic composite designs while reducing the need for repeated FEM evaluations.