Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning

Abstract

Understanding and controlling the self-assembly of vertically oriented carbon nanotube (CNT) forests is essential for realizing their potential in myriad applications. The governing process–structure–property mechanisms are poorly understood, and the processing parameter space is far too vast to exhaustively explore experimentally. We overcome these limitations by using a physics-based simulation as a high-throughput virtual laboratory and image-based machine learning to relate CNT forest synthesis attributes to their mechanical performance. Using CNTNet, our image-based deep learning classifier module trained with synthetic imagery, combinations of CNT diameter, density, and population growth rate classes were labeled with an accuracy of >91%. The CNTNet regression module predicted CNT forest stiffness and buckling load properties with a lower root-mean-square error than that of a regression predictor based on CNT physical parameters. These results demonstrate that image-based machine learning trained using only simulated imagery can distinguish subtle CNT forest morphological features to predict physical material properties with high accuracy. CNTNet paves the way to incorporate scanning electron microscope imagery for high-throughput material discovery.

Influence of gadolinium doping on structural properties of carbon nanotubes

“…Gd-MWCNTs are wavy, entangled, interconnected with each other, and assembled into 3D net-work architecture. Similar morphology was analyzed in [9].…”

Bridging Nanomanufacturing and Artificial Intelligence—A Comprehensive Review

“…ANN [91] Perovskite oxides ML analysis of perovskite oxides grown by molecular beam epitaxy. K means [92] CNT forest Using ML to predict carbon nanotube forest attributes DL, RFF [93] SrRuO 3 Utilized BO in molecular beam epitaxy BO [94] CsPbBr3 Utilized active learning algorithm to determine the optimal synthesis route Active learning algorithm [95] Nanocomposites Nanocomposites, heterogeneous materials with structures, components, or phases ranging from 1 to 100 nm, find applications in diverse fields, including food, biomedical [96][97][98][99], and electroanalysis [26,[100][101][102]. They offer a unique approach for enhancing properties through the integration of nanoscale reinforcements developed through material synthesis.…”

Deep learning of crystalline defects from TEM images: a solution for the problem of ‘never enough training data’

“…There are-in principle-many methods and models readily available, covering all phenomena from atomistic to macroscopic scales. Such simulations can be used to generate synthetic data as shown by Hajilounezhad et al [38] where a scanning electron microscopy (SEM) simulation tool is used to get artificial images of a carbon nanotube forest along with the calculated properties of the structures used as ground truth. In another study, microstructure representations are generated using simulation models, which are then rendered to obtain simulated images [39].…”