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.
Exploration of Carbon Nanotube Forest Synthesis-Structure Relationships Using Physics-Based Simulation and Machine Learning
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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- Award ID(s):
- 1651538
- NSF-PAR ID:
- 10156433
- Date Published:
- Journal Name:
- IEEE Applied Imagery Pattern Recognition Workshop
- ISSN:
- 2164-2516
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract -
While the physical properties of carbon nanotubes (CNTs) are often superior to conventional engineering materials, their widespread adoption into many applications is limited by scaling the properties of individual CNTs to macroscale CNT assemblies known as CNT forests. The self-assembly mechanics of CNT forests that determine their morphology and ensemble properties remain poorly understood. Few experimental techniques exist to characterize and observe the growth and self-assembly processes in situ. Here we introduce the use of in-situ scanning electron microscope (SEM) synthesis based on chemical vapor deposition (CVD) processing. In this preliminary report, we share best practices for in-situ SEM CVD processing and initial CNT forest synthesis results. Image analysis techniques are developed to identify and track the movement of catalyst nanoparticles during synthesis conditions. Finally, a perspective is provided in which in-situ SEM observations represent one component of a larger system in which numerical simulation, machine learning, and digital control of experiments reduces the role of humans and human error in the exploration of CNT forest process-structure-property relationships.more » « less
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Carbon nanotube (CNT) forests are imaged using scanning electron microscopes (SEMs) that project their multilayered 3D structure into a single 2D image. Image analytics, particularly instance segmentation is needed to quantify structural characteristics and to predict correlations between structural morphology and physical properties. The inherent complexity of individual CNT structures is further increased in CNT forests due to density of CNTs, interactions between CNTs, occlusions, and lack of 3D information to resolve correspondences when multiple CNTs from different depths appear to cross in 2D. In this paper, we propose CNT-NeRF, a generative adversarial network (GAN) for simultaneous depth layer decomposition and segmentation of CNT forests in SEM images. The proposed network is trained using a multi-layer, photo-realistic synthetic dataset obtained by transferring the style of real CNT images to physics-based simulation data. Experiments show promising depth layer decomposition and accurate CNT segmentation results not only for the front layer but also for the partially occluded middle and back layers. This achievement is a significant step towards automated, image-based CNT forest structure characterization and physical property prediction.more » « less
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A time-resolved two-dimensional finite element simulation is developed to model the forces generated during the self-assembly of actively growing CNT populations with distributed properties and growth characteristics. CNTs are simulated as interconnected frame elements that undergo the base growth mechanism. Mechanical equilibrium at each computational node is determined at each time step using the Updated Lagrangian method. Emphasis is placed on the transmission of force to the growth substrate, where catalyst particles reside. The simulated CNT forest structural morphology is similar to that of physical CNT forests, and results indicate that stresses on the order of GPa are transmitted to catalyst particles. The force transmitted to a given catalyst particle is correlated to the rate at which the CNT grows relative to the population averaged growth rate. The effect of diameter-dependent CNT growth rates and the persistence of vdW bonds are also examined relative to the forces generated during forest self-assembly. Results from this study may be applied to the study of CNT forest self-assembly, resultant ensemble forest properties, and force-modulated CNT growth kinetics.more » « less
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Abstract Modern machine learning (ML) and deep learning (DL) techniques using high-dimensional data representations have helped accelerate the materials discovery process by efficiently detecting hidden patterns in existing datasets and linking input representations to output properties for a better understanding of the scientific phenomenon. While a deep neural network comprised of fully connected layers has been widely used for materials property prediction, simply creating a deeper model with a large number of layers often faces with vanishing gradient problem, causing a degradation in the performance, thereby limiting usage. In this paper, we study and propose architectural principles to address the question of improving the performance of model training and inference under fixed parametric constraints. Here, we present a general deep-learning framework based on branched residual learning (BRNet) with fully connected layers that can work with any numerical vector-based representation as input to build accurate models to predict materials properties. We perform model training for materials properties using numerical vectors representing different composition-based attributes of the respective materials and compare the performance of the proposed models against traditional ML and existing DL architectures. We find that the proposed models are significantly more accurate than the ML/DL models for all data sizes by using different composition-based attributes as input. Further, branched learning requires fewer parameters and results in faster model training due to better convergence during the training phase than existing neural networks, thereby efficiently building accurate models for predicting materials properties.more » « less
- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/https://doi.org/10.31224/osf.io/7tqam
