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