IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v12y2021i1d10.1038_s41467-021-26511-5.html
   My bibliography  Save this article

Robust recognition and exploratory analysis of crystal structures via Bayesian deep learning

Author

Listed:
  • Andreas Leitherer

    (Fritz-Haber-Institut der Max-Planck-Gesellschaft)

  • Angelo Ziletti

    (Fritz-Haber-Institut der Max-Planck-Gesellschaft)

  • Luca M. Ghiringhelli

    (Fritz-Haber-Institut der Max-Planck-Gesellschaft)

Abstract

Due to their ability to recognize complex patterns, neural networks can drive a paradigm shift in the analysis of materials science data. Here, we introduce ARISE, a crystal-structure identification method based on Bayesian deep learning. As a major step forward, ARISE is robust to structural noise and can treat more than 100 crystal structures, a number that can be extended on demand. While being trained on ideal structures only, ARISE correctly characterizes strongly perturbed single- and polycrystalline systems, from both synthetic and experimental resources. The probabilistic nature of the Bayesian-deep-learning model allows to obtain principled uncertainty estimates, which are found to be correlated with crystalline order of metallic nanoparticles in electron tomography experiments. Applying unsupervised learning to the internal neural-network representations reveals grain boundaries and (unapparent) structural regions sharing easily interpretable geometrical properties. This work enables the hitherto hindered analysis of noisy atomic structural data from computations or experiments.

Suggested Citation

  • Andreas Leitherer & Angelo Ziletti & Luca M. Ghiringhelli, 2021. "Robust recognition and exploratory analysis of crystal structures via Bayesian deep learning," Nature Communications, Nature, vol. 12(1), pages 1-13, December.
  • Handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26511-5
    DOI: 10.1038/s41467-021-26511-5
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-021-26511-5
    File Function: Abstract
    Download Restriction: no

    File URL: https://libkey.io/10.1038/s41467-021-26511-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Angelo Ziletti & Devinder Kumar & Matthias Scheffler & Luca M. Ghiringhelli, 2018. "Insightful classification of crystal structures using deep learning," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    2. Chien-Chun Chen & Chun Zhu & Edward R. White & Chin-Yi Chiu & M. C. Scott & B. C. Regan & Laurence D. Marks & Yu Huang & Jianwei Miao, 2013. "Three-dimensional imaging of dislocations in a nanoparticle at atomic resolution," Nature, Nature, vol. 496(7443), pages 74-77, April.
    3. Peter Rez & Michael M. J. Treacy, 2013. "Three-dimensional imaging of dislocations," Nature, Nature, vol. 503(7476), pages 1-1, November.
    4. Yongsoo Yang & Chien-Chun Chen & M. C. Scott & Colin Ophus & Rui Xu & Alan Pryor & Li Wu & Fan Sun & Wolfgang Theis & Jihan Zhou & Markus Eisenbach & Paul R. C. Kent & Renat F. Sabirianov & Hao Zeng &, 2017. "Deciphering chemical order/disorder and material properties at the single-atom level," Nature, Nature, vol. 542(7639), pages 75-79, February.
    5. Thorsten Meiners & Timofey Frolov & Robert E. Rudd & Gerhard Dehm & Christian H. Liebscher, 2020. "Observations of grain-boundary phase transformations in an elemental metal," Nature, Nature, vol. 579(7799), pages 375-378, March.
    6. Jihan Zhou & Yongsoo Yang & Yao Yang & Dennis S. Kim & Andrew Yuan & Xuezeng Tian & Colin Ophus & Fan Sun & Andreas K. Schmid & Michael Nathanson & Hendrik Heinz & Qi An & Hao Zeng & Peter Ercius & Ji, 2019. "Observing crystal nucleation in four dimensions using atomic electron tomography," Nature, Nature, vol. 570(7762), pages 500-503, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Philipp M. Pelz & Sinéad M. Griffin & Scott Stonemeyer & Derek Popple & Hannah DeVyldere & Peter Ercius & Alex Zettl & Mary C. Scott & Colin Ophus, 2023. "Solving complex nanostructures with ptychographic atomic electron tomography," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    2. Linze Li & Bin Ouyang & Zhengyan Lun & Haoyan Huo & Dongchang Chen & Yuan Yue & Colin Ophus & Wei Tong & Guoying Chen & Gerbrand Ceder & Chongmin Wang, 2023. "Atomic-scale probing of short-range order and its impact on electrochemical properties in cation-disordered oxide cathodes," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    3. Zezhou Li & Zhiheng Xie & Yao Zhang & Xilong Mu & Jisheng Xie & Hai-Jing Yin & Ya-Wen Zhang & Colin Ophus & Jihan Zhou, 2023. "Probing the atomically diffuse interfaces in Pd@Pt core-shell nanoparticles in three dimensions," Nature Communications, Nature, vol. 14(1), pages 1-10, December.
    4. Xiao Han & Yanan Zhou & Xiaolin Tai & Geng Wu & Cai Chen & Xun Hong & Lei Tong & Fangfang Xu & Hai-Wei Liang & Yue Lin, 2024. "In-situ atomic tracking of intermetallic compound formation during thermal annealing," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    5. Hyesung Jo & Dae Han Wi & Taegu Lee & Yongmin Kwon & Chaehwa Jeong & Juhyeok Lee & Hionsuck Baik & Alexander J. Pattison & Wolfgang Theis & Colin Ophus & Peter Ercius & Yea-Lee Lee & Seunghwa Ryu & Sa, 2022. "Direct strain correlations at the single-atom level in three-dimensional core-shell interface structures," Nature Communications, Nature, vol. 13(1), pages 1-13, December.
    6. Chaehwa Jeong & Juhyeok Lee & Hyesung Jo & Jaewhan Oh & Hionsuck Baik & Kyoung-June Go & Junwoo Son & Si-Young Choi & Sergey Prosandeev & Laurent Bellaiche & Yongsoo Yang, 2024. "Revealing the three-dimensional arrangement of polar topology in nanoparticles," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    7. Tore Niermann & Laura Niermann & Michael Lehmann, 2024. "Three dimensional classification of dislocations from single projections," Nature Communications, Nature, vol. 15(1), pages 1-7, December.
    8. Enze Chen & Tae Wook Heo & Brandon C. Wood & Mark Asta & Timofey Frolov, 2024. "Grand canonically optimized grain boundary phases in hexagonal close-packed titanium," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    9. Takehito Seki & Toshihiro Futazuka & Nobusato Morishige & Ryo Matsubara & Yuichi Ikuhara & Naoya Shibata, 2023. "Incommensurate grain-boundary atomic structure," Nature Communications, Nature, vol. 14(1), pages 1-7, December.
    10. Yao Zhang & Zezhou Li & Xing Tong & Zhiheng Xie & Siwei Huang & Yue-E Zhang & Hai-Bo Ke & Wei-Hua Wang & Jihan Zhou, 2024. "Three-dimensional atomic insights into the metal-oxide interface in Zr-ZrO2 nanoparticles," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    11. Vicky Zampeta & Gregory Chondrokoukis, 2023. "Maritime Transportation Accidents: A Bibliometric Analysis," International Journal of Business and Economic Sciences Applied Research (IJBESAR), Democritus University of Thrace (DUTH), Kavala Campus, Greece, vol. 16(1), pages 19-26, October.
    12. Yuwei Mao & Hui Lin & Christina Xuan Yu & Roger Frye & Darren Beckett & Kevin Anderson & Lars Jacquemetton & Fred Carter & Zhangyuan Gao & Wei-keng Liao & Alok N. Choudhary & Kornel Ehmann & Ankit Agr, 2023. "A deep learning framework for layer-wise porosity prediction in metal powder bed fusion using thermal signatures," Journal of Intelligent Manufacturing, Springer, vol. 34(1), pages 315-329, January.
    13. Xuyang Zhou & Ali Ahmadian & Baptiste Gault & Colin Ophus & Christian H. Liebscher & Gerhard Dehm & Dierk Raabe, 2023. "Atomic motifs govern the decoration of grain boundaries by interstitial solutes," Nature Communications, Nature, vol. 14(1), pages 1-11, December.
    14. Vasileios Maroulas & Cassie Putman Micucci & Adam Spannaus, 2020. "A stable cardinality distance for topological classification," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(3), pages 611-628, September.
    15. Pessa, Arthur A.B. & Zola, Rafael S. & Perc, Matjaž & Ribeiro, Haroldo V., 2022. "Determining liquid crystal properties with ordinal networks and machine learning," Chaos, Solitons & Fractals, Elsevier, vol. 154(C).
    16. Binglu Zhang & Qisi Zhu & Chi Xu & Changtai Li & Yuan Ma & Zhaoxiang Ma & Sinuo Liu & Ruiwen Shao & Yuting Xu & Baolong Jiang & Lei Gao & Xiaolu Pang & Yang He & Guang Chen & Lijie Qiao, 2022. "Atomic-scale insights on hydrogen trapping and exclusion at incoherent interfaces of nanoprecipitates in martensitic steels," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    17. Jonathan Schwartz & Chris Harris & Jacob Pietryga & Huihuo Zheng & Prashant Kumar & Anastasiia Visheratina & Nicholas A. Kotov & Brianna Major & Patrick Avery & Peter Ercius & Utkarsh Ayachit & Berk G, 2022. "Real-time 3D analysis during electron tomography using tomviz," Nature Communications, Nature, vol. 13(1), pages 1-7, December.
    18. Lena Langenohl & Tobias Brink & Rodrigo Freitas & Timofey Frolov & Gerhard Dehm & Christian H. Liebscher, 2022. "Dual phase patterning during a congruent grain boundary phase transition in elemental copper," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    19. Riga Wu & Yuan Yu & Shuo Jia & Chongjian Zhou & Oana Cojocaru-Mirédin & Matthias Wuttig, 2023. "Strong charge carrier scattering at grain boundaries of PbTe caused by the collapse of metavalent bonding," Nature Communications, Nature, vol. 14(1), pages 1-8, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:12:y:2021:i:1:d:10.1038_s41467-021-26511-5. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.