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

TL;DR

This paper introduces a deep learning pipeline that uses multi-layer synthetic images to accurately predict the mechanical properties of carbon nanotube forests, reducing reliance on costly 3D simulations.

Contribution

The study presents a novel data augmentation technique with MLS images and a new neural network architecture, CNTNeXt, for improved property prediction of CNTs from SEM images.

Findings

MLS images improve prediction accuracy over single synthetic images

CNTNeXt outperforms previous models in estimating mechanical properties

Method reduces need for expensive 3D simulations or experiments

Abstract

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.