Rapid Identification of X-ray Diffraction Patterns Based on Very Limited Data by Interpretable Convolutional Neural Networks

Wang, Hong; Xie, Yunchao; Li, Dawei; Deng, Heng; Zhao, Yunxin; Xin, Ming; Lin, Jian

doi:10.1021/acs.jcim.0c00020

Title: Rapid Identification of X-ray Diffraction Patterns Based on Very Limited Data by Interpretable Convolutional Neural Networks

Journal Article · Wed Mar 25 00:00:00 EDT 2020 · Journal of Chemical Information and Modeling

DOI:https://doi.org/10.1021/acs.jcim.0c00020· OSTI ID:1853798

Wang, Hong ^[1];

^[1]; Li, Dawei ^[1]; Deng, Heng ^[1]; Zhao, Yunxin ^[1]; Xin, Ming ^[1];

^[1]

Univ. of Missouri, Columbia, MO (United States)

Large volumes of data from material characterizations call for rapid and automatic data analysis to accelerate materials discovery. Herein, we report a convolutional neural network (CNN) that was trained based on theoretical data and very limited experimental data for fast identification of experimental X-ray diffraction (XRD) patterns of metal–organic frameworks (MOFs). To augment the data for training the model, noise was extracted from experimental data and shuffled; then it was merged with the main peaks that were extracted from theoretical spectra to synthesize new spectra. For the first time, one-to-one material identification was achieved. Theoretical MOFs patterns (1012) were augmented to a whole data set of 72 864 samples. It was then randomly shuffled and split into training (58 292 samples) and validation (14 572 samples) data sets at a ratio of 4:1. For the task of discriminating, the optimized model showed the highest identification accuracy of 96.7% for the top 5 ranking on a test data set of 30 hold-out samples. Neighborhood component analysis (NCA) on the experimental XRD samples shows that the samples from the same material are clustered in groups in the NCA map. Analysis on the class activation maps of the last CNN layer further discloses the mechanism by which the CNN model successfully identifies individual MOFs from the XRD patterns. Furthermore, this CNN model trained by the data augmentation technique would not only open numerous potential applications for identifying XRD patterns for different materials, but also pave avenues to autonomously analyze data by other characterization tools such as FTIR, Raman, and NMR spectroscopies.

View Accepted Manuscript (DOE)

Cite

Export

Save

Research Organization:: Univ. of Missouri, Columbia, MO (United States)

Sponsoring Organization:: USDOE

Grant/Contract Number:: FE0031645

OSTI ID:: 1853798

Journal Information:: Journal of Chemical Information and Modeling, Vol. 60, Issue 4; ISSN 1549-9596

Publisher:: American Chemical SocietyCopyright Statement

Country of Publication:: United States

Language:: English

References (30)

Accelerating Materials Development via Automation, Machine Learning, and High-Performance Computing Correa-Baena, Juan-Pablo; Hippalgaonkar, Kedar; van Duren, Jeroen Joule, Vol. 2, Issue 8 https://doi.org/10.1016/j.joule.2018.05.009	journal	August 2018
Fast and interpretable classification of small X-ray diffraction datasets using data augmentation and deep neural networks Oviedo, Felipe; Ren, Zekun; Sun, Shijing npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0196-x	journal	May 2019
Neural network based classification of crystal symmetries from x-ray diffraction patterns Vecsei, Pascal Marc; Choo, Kenny; Chang, Johan Physical Review B, Vol. 99, Issue 24 https://doi.org/10.1103/PhysRevB.99.245120	journal	June 2019
A robotic platform for flow synthesis of organic compounds informed by AI planning Coley, Connor W.; Thomas, Dale A.; Lummiss, Justin A. M. Science, Vol. 365, Issue 6453 https://doi.org/10.1126/science.aax1566	journal	August 2019
Organic synthesis in a modular robotic system driven by a chemical programming language Steiner, Sebastian; Wolf, Jakob; Glatzel, Stefan Science, Vol. 363, Issue 6423 https://doi.org/10.1126/science.aav2211	journal	November 2018
Controlling an organic synthesis robot with machine learning to search for new reactivity Granda, Jarosław M.; Donina, Liva; Dragone, Vincenza Nature, Vol. 559, Issue 7714 https://doi.org/10.1038/s41586-018-0307-8	journal	July 2018
Universal fragment descriptors for predicting properties of inorganic crystals Isayev, Olexandr; Oses, Corey; Toher, Cormac Nature Communications, Vol. 8, Issue 1 https://doi.org/10.1038/ncomms15679	journal	June 2017
New frontiers for the materials genome initiative de Pablo, Juan J.; Jackson, Nicholas E.; Webb, Michael A. npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0173-4	journal	April 2019
Machine learning for molecular and materials science Butler, Keith T.; Davies, Daniel W.; Cartwright, Hugh Nature, Vol. 559, Issue 7715 https://doi.org/10.1038/s41586-018-0337-2	journal	July 2018
Discovery of Intermetallic Compounds from Traditional to Machine-Learning Approaches Oliynyk, Anton O.; Mar, Arthur Accounts of Chemical Research, Vol. 51, Issue 1 https://doi.org/10.1021/acs.accounts.7b00490	journal	December 2017
Design of self-assembly dipeptide hydrogels and machine learning via their chemical features Li, Fei; Han, Jinsong; Cao, Tian Proceedings of the National Academy of Sciences, Vol. 116, Issue 23 https://doi.org/10.1073/pnas.1903376116	journal	May 2019
A general-purpose machine learning framework for predicting properties of inorganic materials Ward, Logan; Agrawal, Ankit; Choudhary, Alok npj Computational Materials, Vol. 2, Issue 1 https://doi.org/10.1038/npjcompumats.2016.28	journal	August 2016
Machine learning-assisted directed protein evolution with combinatorial libraries Wu, Zachary; Kan, S. B. Jennifer; Lewis, Russell D. Proceedings of the National Academy of Sciences, Vol. 116, Issue 18 https://doi.org/10.1073/pnas.1901979116	journal	April 2019
Insightful classification of crystal structures using deep learning Ziletti, Angelo; Kumar, Devinder; Scheffler, Matthias Nature Communications, Vol. 9, Issue 1 https://doi.org/10.1038/s41467-018-05169-6	journal	July 2018
Decoding crystallography from high-resolution electron imaging and diffraction datasets with deep learning Aguiar, J. A.; Gong, M. L.; Unocic, R. R. Science Advances, Vol. 5, Issue 10 https://doi.org/10.1126/sciadv.aaw1949	journal	October 2019
Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries Iwasaki, Yuma; Kusne, A. Gilad; Takeuchi, Ichiro npj Computational Materials, Vol. 3, Issue 1 https://doi.org/10.1038/s41524-017-0006-2	journal	February 2017
High-throughput determination of structural phase diagram and constituent phases using GRENDEL Kusne, A. G.; Keller, D.; Anderson, A. Nanotechnology, Vol. 26, Issue 44 https://doi.org/10.1088/0957-4484/26/44/444002	journal	October 2015
Unsupervised phase mapping of X-ray diffraction data by nonnegative matrix factorization integrated with custom clustering Stanev, Valentin; Vesselinov, Velimir V.; Kusne, A. Gilad npj Computational Materials, Vol. 4, Issue 1 https://doi.org/10.1038/s41524-018-0099-2	journal	August 2018
Crystallographic prediction from diffraction and chemistry data for higher throughput classification using machine learning Aguiar, Jeffery A.; Gong, Matthew L.; Tasdizen, Tolga Computational Materials Science, Vol. 173 https://doi.org/10.1016/j.commatsci.2019.109409	journal	February 2020
Unsupervised classification of single-particle X-ray diffraction snapshots by spectral clustering Yoon, Chun Hong; Schwander, Peter; Abergel, Chantal Optics Express, Vol. 19, Issue 17 https://doi.org/10.1364/OE.19.016542	journal	January 2011
Deep Learning of Atomically Resolved Scanning Transmission Electron Microscopy Images: Chemical Identification and Tracking Local Transformations Ziatdinov, Maxim; Dyck, Ondrej; Maksov, Artem ACS Nano, Vol. 11, Issue 12 https://doi.org/10.1021/acsnano.7b07504	journal	October 2016
A deep-learning technique for phase identification in multiphase inorganic compounds using synthetic XRD powder patterns Lee, Jin-Woong; Park, Woon Bae; Lee, Jin Hee Nature Communications, Vol. 11, Issue 1 https://doi.org/10.1038/s41467-019-13749-3	journal	January 2020
Classification of crystal structure using a convolutional neural network Park, Woon Bae; Chung, Jiyong; Jung, Jaeyoung IUCrJ, Vol. 4, Issue 4 https://doi.org/10.1107/S205225251700714X	journal	June 2017
Automated estimation of materials parameter from X-ray absorption and electron energy-loss spectra with similarity measures Suzuki, Yuta; Hino, Hideitsu; Kotsugi, Masato npj Computational Materials, Vol. 5, Issue 1 https://doi.org/10.1038/s41524-019-0176-1	journal	March 2019
Development of a Cambridge Structural Database Subset: A Collection of Metal–Organic Frameworks for Past, Present, and Future Moghadam, Peyman Z.; Li, Aurelia; Wiggin, Seth B. Chemistry of Materials, Vol. 29, Issue 7 https://doi.org/10.1021/acs.chemmater.7b00441	journal	March 2017
Gradient-based learning applied to document recognition Lecun, Y.; Bottou, L.; Bengio, Y. Proceedings of the IEEE, Vol. 86, Issue 11 https://doi.org/10.1109/5.726791	journal	January 1998
Deep learning LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey Nature, Vol. 521, Issue 7553 https://doi.org/10.1038/nature14539	journal	May 2015
Deep learning for sensor-based activity recognition: A survey Wang, Jindong; Chen, Yiqiang; Hao, Shuji Pattern Recognition Letters, Vol. 119 https://doi.org/10.1016/j.patrec.2018.02.010	journal	March 2019
MultiCAM: Multiple Class Activation Mapping for Aircraft Recognition in Remote Sensing Images Fu, Kun; Dai, Wei; Zhang, Yue Remote Sensing, Vol. 11, Issue 5 https://doi.org/10.3390/rs11050544	journal	March 2019
Learning Deep Features for Discriminative Localization Zhou, Bolei; Khosla, Aditya; Lapedriza, Agata 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) https://doi.org/10.1109/CVPR.2016.319	conference	June 2016

Similar Records

A Look Inside the Black Box: Using graph-theoretical descriptors to interpret a Continuous-Filter Convolutional Neural Network (CF-CNN) trained on the global and local minimum energy structures of neutral water clusters

Journal Article · Tue Jul 14 00:00:00 EDT 2020 · Journal of Chemical Physics · OSTI ID:1853798

Bilbrey, Jenna A.; Heindel, Joseph; Schram, Malachi; +3 more

Classification and Localization of Fracture-Hit Events in Low-Frequency Distributed Acoustic Sensing Strain Rate with Convolutional Neural Networks

Journal Article · Fri Jan 21 00:00:00 EST 2022 · SPE Journal · OSTI ID:1853798

Chen, Mengyuan; Tang, Jin; Zhu, Ding; +1 more

Integrated analysis of X-ray diffraction patterns and pair distribution functions for machine-learned phase identification

Journal Article · Wed Feb 28 00:00:00 EST 2024 · npj Computational Materials · OSTI ID:1853798

Szymanski, Nathan J.; Fu, Sean; Persson, Ellen; +1 more

Related Subjects

36 MATERIALS SCIENCE
Layers
Metal organic frameworks
Physical and chemical processes
Theoretical models
Materials

Title: Rapid Identification of X-ray Diffraction Patterns Based on Very Limited Data by Interpretable Convolutional Neural Networks

Citation Formats

References (30)

Similar Records

Related Subjects