摘要
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.
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-SC0021110
K.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
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-AC02-05CH11231
This 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.
