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.
Highlights
The timeline for materials discovery, development, and deployment is slow and resource intensive, requiring 10–20 years of research and development to bring a product to market[1]
Realizing properties within this envelope will require the development of new tools that can identify how carbon nanotube (CNT) forest synthesis attributes correlate with forest structure and ensemble physical properties
The results demonstrate that image-based machine learning (ML) algorithms using simulated scanning electron microscope (SEM) imagery are able to detect unique visual structural morphological characteristics of CNT forests to provide precise, individualized predictions
Summary
The timeline for materials discovery, development, and deployment is slow and resource intensive, requiring 10–20 years of research and development to bring a product to market[1]. While the existing performance gap between predicted and observed properties is vast, it serves as an indicator that CNT forests may be designed to operate within a larger technological performance envelope if the CNT forest assembly processes could be better understood and controlled Realizing properties within this envelope will require the development of new tools that can identify how CNT forest synthesis attributes correlate with forest structure and ensemble physical properties. A total of 63 unique CNT forest synthesis classes were established based on combinations of CNT diameter, population growth rate variability, and CNT areal density From these classes, a combined pool of 22,106 FEM simulated synthesis and compression experiments were performed generating one image per experiment to create a pool of data for training and testing the ML models. The results demonstrate that image-based ML algorithms using simulated SEM imagery are able to detect unique visual structural morphological characteristics of CNT forests to provide precise, individualized predictions
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