The parameter space of CNT forest synthesis is vast
and multidimensional, making experimental and/or numerical
exploration of the synthesis prohibitive. We propose a more
practical approach to explore the synthesis-process relationships
of CNT forests using machine learning (ML) algorithms to
infer the underlying complex physical processes. Currently, no
such ML model linking CNT forest morphology to synthesis
parameters has been demonstrated. In the current work, we
use a physics-based numerical model to generate CNT forest
morphology images with known synthesis parameters to train
such a ML algorithm. The CNT forest synthesis variables
of CNT diameter and CNT number densities are varied to
generate a total of 12 distinct CNT forest classes. Images of the
resultant CNT forests at different time steps during the growth
and self-assembly process are then used as the training dataset.
Based on the CNT forest structural morphology, multiple
single and combined histogram-based texture descriptors are
used as features to build a random forest (RF) classifier to
predict class labels based on correlation of CNT forest physical
attributes with the growth parameters. The machine learning
model achieved an accuracy of up to 83.5% on predicting the
synthesis conditions of CNT number density and diameter.
These results are the first step towards rapidly characterizing
CNT forest attributes using machine learning. Identifying the
relevant process-structure interactions for the CNT forests using
physics-based simulations and machine learning could rapidly
advance the design, development, and adoption of CNT forest
applications with varied morphologies and properties
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Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning
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.
more » « less- Award ID(s):
- 2026847
- NSF-PAR ID:
- 10383552
- Publisher / Repository:
- Nature Publishing Group
- Date Published:
- Journal Name:
- npj Computational Materials
- Volume:
- 7
- Issue:
- 1
- ISSN:
- 2057-3960
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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