Atomic column heights detection in metallic nanoparticles using deep convolutional learning
暂无分享,去创建一个
Farzad Mashayek | Reza Shahbazian-Yassar | Marco Ragone | Vitaliy Yurkiv | F. Mashayek | Boao Song | R. Shahbazian‐Yassar | V. Yurkiv | Marco Ragone | Ajaykrishna Ramsubramanian | Boao Song | Ajaykrishna Ramsubramanian | Vitaliy Yurkiv
[1] Jingchao Zhang,et al. Machine learning and artificial neural network prediction of interfacial thermal resistance between graphene and hexagonal boron nitride. , 2018, Nanoscale.
[2] Colin Ophus,et al. Three-dimensional coordinates of individual atoms in materials revealed by electron tomography. , 2015, Nature materials.
[3] S. Bals,et al. Procedure to count atoms with trustworthy single-atom sensitivity , 2013 .
[4] Susanne Stemmer,et al. Standardless atom counting in scanning transmission electron microscopy. , 2010, Nano letters.
[5] A. Thust,et al. Maximum-likelihood method for focus-variation image reconstruction in high resolution transmission electron microscopy , 1996 .
[6] Liang Zheng,et al. Multi-channel convolutional neural networks for materials properties prediction , 2020, Computational Materials Science.
[7] C. Jia,et al. Determination of the 3D shape of a nanoscale crystal with atomic resolution from a single image. , 2014, Nature materials.
[8] Qiao Zhang,et al. Recent advances in noble metal based composite nanocatalysts: colloidal synthesis, properties, and catalytic applications. , 2015, Nanoscale.
[9] J. Sijbers,et al. Estimation of unknown structure parameters from high-resolution (S)TEM images: what are the limits? , 2013, Ultramicroscopy.
[10] D. Van dyck,et al. 3D reconstruction of nanocrystalline particles from a single projection. , 2015, Micron.
[11] Rama Vasudevan,et al. Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations. , 2017, ACS nano.
[12] Geoffrey E. Hinton,et al. ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.
[13] B. Wiley,et al. On the road to metallic nanoparticles by rational design: bridging the gap between atomic-level theoretical modeling and reality by total scattering experiments. , 2015, Nanoscale.
[14] Martin Hÿtch,et al. Quantitative measurement of displacement and strain fields from HREM micrographs , 1998 .
[15] Geoffrey E. Hinton,et al. Deep Learning , 2015, Nature.
[16] Sven Behnke,et al. Evaluation of Pooling Operations in Convolutional Architectures for Object Recognition , 2010, ICANN.
[17] T. Zhao,et al. Mesoscale Modelling of Concretes Subjected to Triaxial Loadings: Mechanical Properties and Fracture Behaviour , 2021, Materials.
[18] Ole Winther,et al. A Deep Learning Approach to Identify Local Structures in Atomic‐Resolution Transmission Electron Microscopy Images , 2018, Advanced Theory and Simulations.
[19] Roberto Cipolla,et al. SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[20] Song Xiang,et al. Modeling of CCT diagrams for tool steels using different machine learning techniques , 2020 .
[21] R. Asahi,et al. Microstructure recognition using convolutional neural networks for prediction of ionic conductivity in ceramics , 2017 .
[22] Andrés Yáñez,et al. The Peak Pairs algorithm for strain mapping from HRTEM images. , 2007, Ultramicroscopy.
[23] Erin Antono,et al. Building Data-driven Models with Microstructural Images: Generalization and Interpretability , 2017, ArXiv.
[24] Rohit Batra,et al. A multi-fidelity information-fusion approach to machine learn and predict polymer bandgap , 2020 .
[25] A. Shapeev,et al. Lattice dynamics simulation using machine learning interatomic potentials , 2020 .
[26] Nathan Li,et al. Multiscale prediction of functional self-assembled materials using machine learning: high-performance surfactant molecules. , 2018, Nanoscale.
[27] Ming Hu,et al. Evaluating explorative prediction power of machine learning algorithms for materials discovery using k-fold forward cross-validation , 2020, Computational Materials Science.
[28] Camille Couprie,et al. Learning Hierarchical Features for Scene Labeling , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[29] Kaiming He,et al. Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[30] Zhenghao Li,et al. An artificial intelligence atomic force microscope enabled by machine learning. , 2018, Nanoscale.
[31] J. Miao,et al. Three-dimensional imaging of dislocations in a nanoparticle at atomic resolution , 2013, Nature.
[32] J. Miao,et al. Electron tomography at 2.4-ångström resolution , 2012, Nature.
[33] A. Thust,et al. Focal-series reconstruction in HRTEM: simulation studies on non-periodic objects , 1996 .
[34] David T. Limmer,et al. 3D structure of individual nanocrystals in solution by electron microscopy , 2015, Science.
[35] Mario Fritz,et al. Advanced Steel Microstructural Classification by Deep Learning Methods , 2017, Scientific Reports.
[36] Elizabeth A. Holm,et al. A computer vision approach for automated analysis and classification of microstructural image data , 2015 .
[37] Natalia M. Litchinitser,et al. Orbital angular momentum microlaser , 2016, Science.
[38] D. Goia,et al. Size control of noble metal clusters and metallic heterostructures through the reduction kinetics of metal precursors. , 2014, Nanoscale.
[39] Fu-Rong Chen,et al. Resolution extension and exit wave reconstruction in complex HREM. , 2004, Ultramicroscopy.
[40] Michael Walter,et al. The atomic simulation environment-a Python library for working with atoms. , 2017, Journal of physics. Condensed matter : an Institute of Physics journal.
[41] R. Henderson,et al. Comparison of optimal performance at 300 keV of three direct electron detectors for use in low dose electron microscopy , 2014, Ultramicroscopy.