Characterizing crystal structures and interfaces down to the atomic level is an important step for designing advanced materials.Modern electron microscopy routinely achieves atomic resolution and is capable to resolve...Characterizing crystal structures and interfaces down to the atomic level is an important step for designing advanced materials.Modern electron microscopy routinely achieves atomic resolution and is capable to resolve complex arrangements of atoms with picometer precision.Here,we present AI-STEM,an automatic,artificial-intelligence based method,for accurately identifying key characteristics from atomic-resolution scanning transmission electron microscopy(STEM)images of polycrystalline materials.The method is based on a Bayesian convolutional neural network(BNN)that is trained only on simulated images.AI-STEM automatically and accurately identifies crystal structure,lattice orientation,and location of interface regions in synthetic and experimental images.The model is trained on cubic and hexagonal crystal structures,yielding classifications and uncertainty estimates,while no explicit information on structural patterns at the interfaces is included during training.This work combines principles from probabilistic modeling,deep learning,and information theory,enabling automatic analysis of experimental,atomic-resolution images.展开更多
基金L.M.G.acknowledges funding from the European Union’s Horizon 2020 research and innovation program,under grant agreements No.951786(NOMAD CoE)and No.740233(TEC1p)Furthermore,the authors acknowledge the Max Planck Computing and Data facility(MPCDF)for computational resources and support,which enabled neural-network training on 1 GPU(Tesla Volta V10032GB)on the Talos machine learning clusterB.C.Y.acknowledges funding from the National Research Foundation(NRF)of Korea under Project Number 2021M3A7C2090586.
文摘Characterizing crystal structures and interfaces down to the atomic level is an important step for designing advanced materials.Modern electron microscopy routinely achieves atomic resolution and is capable to resolve complex arrangements of atoms with picometer precision.Here,we present AI-STEM,an automatic,artificial-intelligence based method,for accurately identifying key characteristics from atomic-resolution scanning transmission electron microscopy(STEM)images of polycrystalline materials.The method is based on a Bayesian convolutional neural network(BNN)that is trained only on simulated images.AI-STEM automatically and accurately identifies crystal structure,lattice orientation,and location of interface regions in synthetic and experimental images.The model is trained on cubic and hexagonal crystal structures,yielding classifications and uncertainty estimates,while no explicit information on structural patterns at the interfaces is included during training.This work combines principles from probabilistic modeling,deep learning,and information theory,enabling automatic analysis of experimental,atomic-resolution images.
