High-throughput scanning electron microscopy(SEM)coupled with classification using neural networks is an ideal method to determine the morphological handedness of large populations of chiral nanoparticles.Automated la...High-throughput scanning electron microscopy(SEM)coupled with classification using neural networks is an ideal method to determine the morphological handedness of large populations of chiral nanoparticles.Automated labeling removes the time-consuming manual labeling of training data,but introduces label error,and subsequently classification error in the trained neural network.Here,we evaluate methods to minimize classification error when training from automated labels of SEM datasets of chiral Tellurium nanoparticles.Using the mirror relationship between images of opposite handed particles,we artificially create populations of varying label error.We analyze the impact of label error rate and training method on the classification error of neural networks on an ideal dataset and on a practical dataset.Of the three training methods considered,we find that a pretraining approach yields the most accurate results across label error rates on ideal datasets,where size and other morphological variables are held constant,but that a co-teaching approach performs the best in practical application.展开更多
Machine learning techniques are attractive options for developing highly-accurate analysis tools for nanomaterials characterization,including high-resolution transmission electron microscopy(HRTEM).However,successfull...Machine learning techniques are attractive options for developing highly-accurate analysis tools for nanomaterials characterization,including high-resolution transmission electron microscopy(HRTEM).However,successfully implementing such machine learning tools can be difficult due to the challenges in procuring sufficiently large,high-quality training datasets from experiments.In this work,we introduce Construction Zone,a Python package for rapid generation of complex nanoscale atomic structures which enables fast,systematic sampling of realistic nanomaterial structures and can be used as a random structure generator for large,diverse synthetic datasets.Using Construction Zone.展开更多
基金Work at the Molecular Foundry was supported by the Office of Science,Office of Basic Energy Sciences,of the US Department of Energy under Contract No.DE-AC02-05CH11231This material is based upon work supported by the National Science Foundation Graduate Research Fellowship under Grant No.DGE-1752814This work was also supported by National Science Foundation STROBE grant DMR-1548924。
文摘High-throughput scanning electron microscopy(SEM)coupled with classification using neural networks is an ideal method to determine the morphological handedness of large populations of chiral nanoparticles.Automated labeling removes the time-consuming manual labeling of training data,but introduces label error,and subsequently classification error in the trained neural network.Here,we evaluate methods to minimize classification error when training from automated labels of SEM datasets of chiral Tellurium nanoparticles.Using the mirror relationship between images of opposite handed particles,we artificially create populations of varying label error.We analyze the impact of label error rate and training method on the classification error of neural networks on an ideal dataset and on a practical dataset.Of the three training methods considered,we find that a pretraining approach yields the most accurate results across label error rates on ideal datasets,where size and other morphological variables are held constant,but that a co-teaching approach performs the best in practical application.
基金supported by the U.S.Department of Energy,Office of Science,Office of Advanced Scientific Computing Research,Department of Energy Computational Science Graduate Fellowship under Award Number DE-SC0021110K.S.was supported by an appointment to the Intelligence Community Postdoctoral Research Fellowship Program at Lawrence Berkeley National Laboratory administered by Oak Ridge Institute for Science and Education(ORISE)through an interagency agreement between the U.S+3 种基金Department of Energy and the Office of the Director of National Intelligence(ODNI)This work was also funded by the US Department of Energy in the program“4D Camera Distillery:From Massive Electron Microscopy Scattering Data to Useful Information with AI/ML.”Work at the Molecular Foundry was supported by the Office of Science,Office of Basic Energy Sciences,of the U.S.Department of Energy under Contract No.DE-AC02-05CH11231This research used resources of the National Energy Research Scientific Computing Center(NERSC),aU.S.Department of Energy Office of Science User Facility located at Lawrence Berkeley National Laboratory,operated under Contract No.DE-AC02-05CH11231 using NERSC award BES-ERCAP0026467.
文摘Machine learning techniques are attractive options for developing highly-accurate analysis tools for nanomaterials characterization,including high-resolution transmission electron microscopy(HRTEM).However,successfully implementing such machine learning tools can be difficult due to the challenges in procuring sufficiently large,high-quality training datasets from experiments.In this work,we introduce Construction Zone,a Python package for rapid generation of complex nanoscale atomic structures which enables fast,systematic sampling of realistic nanomaterial structures and can be used as a random structure generator for large,diverse synthetic datasets.Using Construction Zone.
