The parameter space of CNT forest synthesis is vastand multidimensional, making experimental and/or numericalexploration of the synthesis prohibitive. We propose a morepractical approach to explore the synthesis-process relationshipsof CNT forests using machine learning (ML) algorithms toinfer the underlying complex physical processes. Currently, nosuch ML model linking CNT forest morphology to synthesisparameters has been demonstrated. In the current work, weuse a physics-based numerical model to generate CNT forestmorphology images with known synthesis parameters to trainsuch a ML algorithm. The CNT forest synthesis variablesof CNT diameter and CNT number densities are varied togenerate a total of 12 distinct CNT forest classes. Images of theresultant CNT forests at different time steps during the growthand self-assembly process are then used as the training dataset.Based on the CNT forest structural morphology, multiplesingle and combined histogram-based texture descriptors areused as features to build a random forest (RF) classifier topredict class labels based on correlation of CNT forest physicalattributes with the growth parameters. The machine learningmodel achieved an accuracy of up to 83.5% on predicting thesynthesis conditions of CNT number density and diameter.These results are the first step towards rapidly characterizingCNT forest attributes using machine learning. Identifying therelevant process-structure interactions for the CNT forests usingphysics-based simulations and machine learning could rapidlyadvance the design, development, and adoption of CNT forestapplications with varied morphologies and properties.

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