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 mechanism...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.展开更多
基金The authors would like to acknowledge funding from National Science Foundation(NSF)under award CCMI 2026847 and CMMI 1651538(for T.H.and M.R.M.)partial support from NSF MRI CNS-1429294 and Army Research Laboratory award W911NF-1820285(for K.P.,R.B.,and F.B.)+1 种基金The computation for this work was performed on a GPU cluster from the Army Research Office DURIP award W911NF1910181Any opinions,findings,and conclusions or recommendations expressed in this publication are those of the authors and do not necessarily reflect the views of the U.S.Government or agency thereof.
文摘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.
