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We present a pipeline for predicting mechanical properties ofvertically-oriented carbon nanotube (CNT) forest images using a deep learningmodel for artificial intelligence (AI)-based materials discovery. Our approachincorporates an innovative data augmentation technique that involves the use ofmulti-layer synthetic (MLS) or quasi-2.5D images which are generated byblending 2D synthetic images. The MLS images more closely resemble 3D syntheticand real scanning electron microscopy (SEM) images of CNTs but without thecomputational cost of performing expensive 3D simulations or experiments.Mechanical properties such as stiffness and buckling load for the MLS imagesare estimated using a physics-based model. The proposed deep learningarchitecture, CNTNeXt, builds upon our previous CNTNet neural network, using aResNeXt feature representation followed by random forest regression estimator.Our machine learning approach for predicting CNT physical properties byutilizing a blended set of synthetic images is expected to outperform singlesynthetic image-based learning when it comes to predicting mechanicalproperties of real scanning electron microscopy images. This has the potentialto accelerate understanding and control of CNT forest self-assembly for diverseapplications.

Predicting mechanical properties of Carbon Nanotube (CNT) images Using Multi-Layer Synthetic Finite Element Model Simulations