Anomaly detection for structural formation analysis by autoencoders: application to soft matters

References

• P.J. Steinhardt, D.R. Nelson, and M. Ronchetti, Bond-orientational order in liquids and glasses, Phys. Rev. B 28 (1983), pp. 784–805.
   Web of Science ®Google Scholar
• P.R. ten Wolde, M.J. Ruiz-Montero, and D. Frenkel, Numerical evidence for BCC ordering at the surface of a critical fcc nucleus, Phys. Rev. Lett. 75 (1995), pp. 2714–2717.
   PubMed Web of Science ®Google Scholar
• W. Lechner and C. Dellago, Accurate determination of crystal structures based on averaged local bond order parameters, J. Chem. Phys. 129 (2008), pp. 114707.
   PubMed Web of Science ®Google Scholar
• L. Roach, A. Hereu, P. Lalanne, E. Duguet, M.T. Delapierre, K. Vynck, and G.L. Drisko, Controlling disorder in self-assembled colloidal monolayers via evaporative processes, Nanoscale. 14 (2022), pp. 3324–3345.
   PubMed Web of Science ®Google Scholar
• P.M. Reis, R.A. Ingale, and M.D. Shattuck, Crystallization of a quasi-Two-dimensional granular fluid, Phys. Rev. Lett. 96 (2006), pp. 258001.
   PubMed Web of Science ®Google Scholar
• F. Moučka and I. Nezbeda, Detection and characterization of structural changes in the hard-disk fluid under freezing and melting conditions, Phys. Rev. Lett. 94 (2005), pp. 040601.
   PubMed Web of Science ®Google Scholar
• V. Lotito and T. Zambelli, Pattern formation in binary colloidal assemblies: hidden symmetries in a kaleidoscope of structures, Langmuir 34 (2018), pp. 7827–7843.
   PubMed Web of Science ®Google Scholar
• F. Galeotti, F. Trespidi, G. Timò, and M. Pasini, Broadband and crack-free antireflection coatings by self-assembled moth Eye patterns, ACS Appl. Mater. Interfaces 6 (2014), pp. 5827–5834.
   PubMed Web of Science ®Google Scholar
• F.J. Dent, D. Harbottle, N.J. Warren, and S. Khodaparast, Temporally arrested breath figure, ACS Appl. Mater. Interfaces 14 (2022), pp. 27435–27443.
   PubMed Web of Science ®Google Scholar
• V. Lotito and T. Zambelli, Pattern detection in colloidal assembly: A mosaic of analysis techniques, Adv. Colloid Interface Sci. 284 (2020), pp. 102252.
   PubMed Web of Science ®Google Scholar
• I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, Cambridge, MA, 2016.
   Google Scholar
• P. Geiger and C. Dellago, Neural networks for local structure detection in polymorphic systems, J. Chem. Phys. 139 (2013), pp. 164105.
   PubMed Web of Science ®Google Scholar
• R.S. DeFever, C. Targonski, S.W. Hall, M.C. Smith, and S.A. Sarupria, Generalized deep learning approach for local structure identification in molecular simulations, Chem. Sci. 10 (2019), pp. 7503–7515.
   PubMed Web of Science ®Google Scholar
• M. Fulford, M. Salvalaglio, and C. Molteni, Deepice: A deep neural network approach to identify Ice and water molecules, J. Chem. Inf. Model 59 (2019), pp. 2141–2149.
   PubMed Web of Science ®Google Scholar
• A.L. Ferguson, Machine learning and data science in soft materials engineering, J. Phys. Condens. Matter. 30 (2018), pp. 043002.
   PubMed Web of Science ®Google Scholar
• C.L. Phillips and G.A. Voth, Discovering crystals using shape matching and machine learning, Soft. Matter. 9 (2013), pp. 8552–8568.
   Web of Science ®Google Scholar
• M. Spellings and S.C. Glotzer, Machine learning for crystal identification and discovery, AIChE J. 64 (2018), pp. 2198–2206.
   Web of Science ®Google Scholar
• T. Terao, A machine learning approach to analyze the structural formation of soft matter via image recognition, Soft. Mater. 18 (2020), pp. 215–227.
   Web of Science ®Google Scholar
• T. Terao, Semi-supervised learning for the study of structural formation in colloidal systems via image recognition, J. Phys. Condens. Matter. 33 (2021), pp. 325901.
   Web of Science ®Google Scholar
• L. Ruff, J.R. Kauffmann, R.A. Vandermeulen, G. Montavon, W. Samek, M. Kloft, T.G. Dietterich, and K.-R. Müller, A unifying review of deep and shallow anomaly detection, Proc. IEEE 109 (2021), pp. 756–795.
   Web of Science ®Google Scholar
• B. Schölkopf, R.C. Williamson, A. Smola, J. Shawe-Taylor, and J. Platt, Support vector method for novelty detection, Adv. Neural. Inf. Process. Syst. 12 (2000), pp. 582–588.
   Google Scholar
• Z. Wang and Y.-J. Cha, Unsupervised deep learning approach using a deep auto-encoder with a one-class support vector machine to detect damage, struct, Health Monit. 20 (2021), pp. 406–425.
   Web of Science ®Google Scholar
• Z. Wang and Y.-J. Cha, Unsupervised machine and deep learning methods for structural damage detection: A comparative study, Eng. Rep. Early View (2022), pp. e12551. https://doi.org/10.1002/eng2.12551
   Google Scholar
• G.E. Hinton and R.R. Salakhutdinov, Reducing the dimensionality of data with neural networks, Science 313 (2006), pp. 504–507.
   PubMed Web of Science ®Google Scholar
• T. Schlegl, P. Seeböck, S.M. Waldstein, U. Schmidt-Erfurth, and G. Langs. Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 10265 LNCS (2017), pp. 146–147.
   Google Scholar
• J. Masci, U. Meier, D. Cireşan, and J. Schmidhuber, Stacked convolutional auto-encoders for hierarchical feature extraction, lecture notes in computer science, in Proceedings of 21st international conference on artificial neural networks, 2011, pp. 52–59.
   Google Scholar
• J.E. Herr, K. Koh, K. Yao, and J. Parkhill, Compressing physics with an autoencoder: Creating an atomic species representation to improve machine learning models in the chemical sciences, J. Chem. Phys. 151 (2019), pp. 084103.
   PubMed Web of Science ®Google Scholar
• P.J. Camp, Structure and phase behavior of a two-dimensional system with core-softened and long-range repulsive interactions, Phys. Rev. E 68 (2003), pp. 061506.
   Web of Science ®Google Scholar
• D. Frenkel and B. Smit, Understanding molecular simulation. 2nd ed.: From Algorithms to Applications, Academic Press, San Diego, 2001.
   Google Scholar
• M.P. Allen and D.J. Tildesley, Computer Simulation of Liquids. 2nd ed., Oxford University Press, Oxford, 2017.
   Google Scholar
• C. Reichhardt and C.J. Olson, Novel colloidal crystalline states on two-dimensional periodic substrates, Phys. Rev. Lett. 88 (2002), pp. 248301.
   PubMed Web of Science ®Google Scholar
• L. McInnes, J. Healy, and J. Melville, UMAP: Uniform manifold approximation and projection for dimension reduction. arXiv:1802.03426 (2018).
   Google Scholar
• L. McInnes, J. Healy, N. Saul, and L. Großberger, Umap: Uniform manifold approximation and projection, J. Open Source Softw. 3 (2018), pp. 861.
   Google Scholar
• L.J.P. van der Maaten and G.E. Hinton, Visualizing data using t-SNE, J. Mach. Learn. Res. 9 (2008), pp. 2579–2605.
   Web of Science ®Google Scholar