Machine Learning for High Throughput HRTEM Analysis

Groschner, Catherine; Choi, Christina; Nguyen, Dat Vinh; Ophus, Colin; Scott, Mary

doi:10.1017/s143192761900148x

Cited by 9 publications

(7 citation statements)

References 9 publications

(10 reference statements)

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“…Generally, this involves building specific architectures for supervised learning (classification or regression), unsupervised latent space extraction, generative models, control systems, and much more. Deep learning has recently been used to extract latent manifolds from high-dimensional spectroscopy, 168 discover phase transformations, 169 segmentation, and detection in microscopy images, 170 and controlled experimentation 171 and atomic manipulation. 150 Despite these successes, there are many open areas for innovation by M3I3.…”

Section: M3i3 Challenges and Future Perspectivementioning

confidence: 99%

Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration

et al. 2021

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Multiscale and multimodal imaging of material structures and properties provides solid ground on which materials theory and design can flourish. Recently, KAIST announced 10 flagship research fields, which include KAIST Materials Revolution: Materials and Molecular Modeling, Imaging, Informatics and Integration (M3I3). The M3I3 initiative aims to reduce the time for the discovery, design and development of materials based on elucidating multiscale processing–structure–property relationship and materials hierarchy, which are to be quantified and understood through a combination of machine learning and scientific insights. In this review, we begin by introducing recent progress on related initiatives around the globe, such as the Materials Genome Initiative (U.S.), Materials Informatics (U.S.), the Materials Project (U.S.), the Open Quantum Materials Database (U.S.), Materials Research by Information Integration Initiative (Japan), Novel Materials Discovery (E.U.), the NOMAD repository (E.U.), Materials Scientific Data Sharing Network (China), Vom Materials Zur Innovation (Germany), and Creative Materials Discovery (Korea), and discuss the role of multiscale materials and molecular imaging combined with machine learning in realizing the vision of M3I3. Specifically, microscopies using photons, electrons, and physical probes will be revisited with a focus on the multiscale structural hierarchy, as well as structure–property relationships. Additionally, data mining from the literature combined with machine learning will be shown to be more efficient in finding the future direction of materials structures with improved properties than the classical approach. Examples of materials for applications in energy and information will be reviewed and discussed. A case study on the development of a Ni–Co–Mn cathode materials illustrates M3I3’s approach to creating libraries of multiscale structure–property–processing relationships. We end with a future outlook toward recent developments in the field of M3I3.

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Section: M3i3 Challenges and Future Perspectivementioning

confidence: 99%

Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration

et al. 2021

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“…Machine learning (machine learning) is a promising approach which is widely used for image analysis in science and beyond. Recently, machine learning has been applied to high-resolution transmission electron microscopy (HRTEM) images to identify the presence of stacking faults [22]. Genetic algorithms and neural networks [15,23] have been implemented to classify nanoparticles [19].…”

Section: Introductionmentioning

confidence: 99%

Automated and manual classification of metallic nanoparticles with respect to size and shape by analysis of scanning electron micrographs

Bals

Loza

Kircher

et al. 2022

Materialwissenschaft Werkst

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Automated image analysis has been applied to scanning electron micrographs (transmission mode; STEM) of metallic nanoparticles (silver and gold; about 10 nm to 20 nm). For a reliable particle identification, scanning electron microscopic images must be recorded with distinct contrast and resolution parameters. The particles were separated from the background and classified according to shape and size by machine learning (machine learning). Training images were created with model particles cut out of real electron microscopic images. The automated analysis of the particle size (expressed as area) was well possible, but overlapping particles could not be safely separated. The assignment of particle to six different shape classes (sphere, triangle, square, pentagon, hexagon, rod) by automated analysis was difficult. The fact that real particles never have an ideal geometrical shape but are always distorted or have rough edges or cropped tips is the fundamental reason of this problem. This effect also occurred with human image evaluators and poses a considerable obstacle in the training process for machine learning. Image analysis by machine learning techniques is difficult if different human evaluators disagree on the shape assignment of given particles because a proper training cannot be provided.

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“…Implementation of machine learning (ML) algorithms is transforming EM methods 11,12 enabling, for example, automated detection and analysis of size and shape distributions of nanoparticles from transmission electron microscopy (TEM), 13,14 scanning electron microscopy (SEM), [15][16][17] helium ion microscopy, 18 and scanning tunneling microscopy 19 images. However, chirality detection requires a more potent methodology of deep learning (DL)a subfield of ML that employs neural networks to solve complex human-like image recognition problems.…”

Section: Introductionmentioning

confidence: 99%

Chirality Analysis for Nanostructured Microparticles Using Deep Learning

Visheratina

Visheratin²,

Kumar

et al. 2022

Preprint

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Chirality of helical objects, exemplified by nanostructured inorganic particles, has unifying importance for many scientific fields. Their handedness can be determined visually, but its identification by analysis of electron microscopy images is fundamentally difficult because (1) image features differentiating left- and right-handed particles can be ambiguous and ancillary, and (2) three-dimensional particle structure essential for chirality is 'flattened' into two-dimensional projections. Here we show that deep learning algorithms can reliably identify and classify twisted bowtie-shaped microparticles in scanning electron microscopy images with accuracy as high as 94.4% having been trained on as few as 180 images. Furthermore, after training on bowtie particles with complex nanostructured features, the model can recognize other chiral shapes with different geometries without re-training. These findings indicate that deep learning can potentially replicate the visual analysis of chiral objects by humans and enable automated analysis of microscopy data for the accelerated discovery of chiral materials.

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Machine Learning for High Throughput HRTEM Analysis

Cited by 9 publications

References 9 publications

Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration

Reducing Time to Discovery: Materials and Molecular Modeling, Imaging, Informatics, and Integration

Automated and manual classification of metallic nanoparticles with respect to size and shape by analysis of scanning electron micrographs

Chirality Analysis for Nanostructured Microparticles Using Deep Learning

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