Classification of crystal structure using a convolutional neural network

Park, Woon Bae; Chung, Ji-Young; Jung, Jason J.; Sohn, Keemin; Singh, Satendra Pal; Pyo, Myoungho; Shin, Namsoo; Sohn, Kee‐Sun

doi:10.1107/s205225251700714x

Cited by 182 publications

(219 citation statements)

References 30 publications

Supporting

Mentioning

212

Contrasting

Order By: Relevance

“…Finally, an application of ML models that has received far less attention in other reviews is their potential to enhance experimental characterization. The interpretation and labeling of experimental images (e.g., scanning transmission electron microscopy (STEM)) and spectra (e.g., X‐ray diffraction (XRD), X‐ray absorption near‐edge structure (XANES), nuclear magnetic resonance (NMR), etc.) are today still mostly painstakingly carried out by humans.…”

Section: Applicationmentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

420

281

View full text Add to dashboard Cite

Machine learning (ML) is rapidly revolutionizing many fields and is starting to change landscapes for physics and chemistry. With its ability to solve complex tasks autonomously, ML is being exploited as a radically new way to help find material correlations, understand materials chemistry, and accelerate the discovery of materials. Here, an in‐depth review of the application of ML to energy materials, including rechargeable alkali‐ion batteries, photovoltaics, catalysts, thermoelectrics, piezoelectrics, and superconductors, is presented. A conceptual framework is first provided for ML in materials science, with a broad overview of different ML techniques as well as best practices. This is followed by a critical discussion of how ML is applied in energy materials. This review is concluded with the perspectives on major challenges and opportunities in this exciting field.

show abstract

Section: Applicationmentioning

confidence: 99%

A Critical Review of Machine Learning of Energy Materials

Chen

Zuo

et al. 2020

Advanced Energy Materials

420

281

View full text Add to dashboard Cite

show abstract

“…Before going into metric analysis, we inspect the raw output of the segmentation. Recall the natural labeler of K clusters is the identity map N (k) = k. We plot the label map φ[v, g, N ] for various choices of parameters, in Figure 7,8,9,10. From these figures, we observe that:…”

Section: Natural Labelermentioning

confidence: 98%

Data-driven approach for synchrotron X-ray Laue microdiffraction scan analysis

Song¹,

Tamura²,

Zhang³

et al. 2019

Acta Cryst Sect A

View full text Add to dashboard Cite

A novel data‐driven approach is proposed for analyzing synchrotron Laue X‐ray microdiffraction scans based on machine learning algorithms. The basic architecture and major components of the method are formulated mathematically. It is demonstrated through typical examples including polycrystalline BaTiO3, multiphase transforming alloys and finely twinned martensite. The computational pipeline is implemented for beamline 12.3.2 at the Advanced Light Source, Lawrence Berkeley National Laboratory. The conventional analytical pathway for X‐ray diffraction scans is based on a slow pattern‐by‐pattern crystal indexing process. This work provides a new way for analyzing X‐ray diffraction 2D patterns, independent of the indexing process, and motivates further studies of X‐ray diffraction patterns from the machine learning perspective for the development of suitable feature extraction, clustering and labeling algorithms.

show abstract

“…Deep convolutional neural networks were recently also applied to synthetic "images" that represent crystallographic symmetry information in 3D [47,48]. Just as in the case of the neural network for 2D Bravais lattice type classifications [46] (as discussed in the fifth section of this paper), pseudosymmetries present challenges to these networks in conjunction with generalized crystal structure data recording and processing noise [44], but were neglected in both of these studies.…”

Section: Appendix: Classifications Of 3d Crystals By Neural Network mentioning

confidence: 99%

“…Note that extinction symbols represent partial space group information, but it is incorrect to refer to these symbols as "extinction groups" as it has been done by Park and co-workers in their paper. Whereas Poisson noise was added to the calculated crystal structure information bearing images, effects of crystallite textures 16 were ignored [47].…”

Section: Appendix: Classifications Of 3d Crystals By Neural Network mentioning

confidence: 99%

“…An appendix discusses the state of the art in classifications of selected aspects of crystal symmetries in 3D by means of DCNNs [47,48] and provides the basis for comparisons of their performance to that of the 2D translation symmetry classifier of [46] (as discussed in the fifth section of this paper). A classification of crystalline compounds by such machines that is aided by the morphologies of individual crystals [49] is also mentioned in the appendix.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

On Classification Approaches for Crystallographic Symmetries of Noisy 2D Periodic Patterns

Moeck

2019

IEEE Trans. Nanotechnology

View full text Add to dashboard Cite

The existing types of classification approaches for the crystallographic symmetries of patterns that are more or less periodic in two dimensions (2D) are reviewed. Their relative performance is evaluated in a qualitative manner. Pseudosymmetries of different kinds are discussed as they present severe challenges to most classification approaches when noise levels are moderate to high. The author's information theory based approaches utilize digital images and geometric Akaike Information Criteria. They perform well in the presence of pseudo-symmetries and turn out to be the only ones that allow for fully objective (completely researcher independent) and generalized noise level dependent classifications of the full range of crystallographic symmetries, i.e. Bravais lattice type, Laue class, and plane symmetry group, of noisy real-world images. His method's identification of the plane symmetry group that can with the highest likelihood be assigned to a noisy 2D periodic image enables the most meaningful crystallographic averaging in the spatial frequency domain. This kind of averaging suppresses generalized noise much more effectively than traditional Fourier filtering. Taking account of the fact that it is fundamentally unsound to assign an abstract mathematical concept such as a single symmetry type, class, or group with 100 % certainty to a more or less 2D periodic record of a noisy real-world imaging experiment that involved a real-world sample, the author's information theory based approaches to crystallographic symmetry classifications deliver probabilistic (rather than definitive) classifications. Recent applications of deep convolutional neural networks (DCNNs) to classifications of crystallographic translation symmetry types in 2D and crystals in three dimensions (3D) are discussed as these "correlation detection and optimization" machines deliver probabilistic classifications by other -non-analytical -means. The discussed DCNN classifications ignore the fact that many crystallographic symmetries are hierarchic, i.e. that the classification classes are often non-disjoint. They are currently also incapable of dealing with pseudo-symmetries. DCNNs for classifications of crystals in 3D are discussed separately in an appendix.

show abstract

Classification of crystal structure using a convolutional neural network

Cited by 182 publications

References 30 publications

A Critical Review of Machine Learning of Energy Materials

A Critical Review of Machine Learning of Energy Materials

Data-driven approach for synchrotron X-ray Laue microdiffraction scan analysis

On Classification Approaches for Crystallographic Symmetries of Noisy 2D Periodic Patterns

Contact Info

Product

Resources

About