2,070
Views
7
CrossRef citations to date
0
Altmetric
New topics/Others

Prediction of viscosity behavior in oxide glass materials using cation fingerprints with artificial neural networks

ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Pages 492-504 | Received 24 Sep 2019, Accepted 22 Jun 2020, Published online: 22 Jul 2020

References

  • Debenedetti PG, Stillinger FH. Supercooled liquids and the glass transition. Nature. 2001;410(6825):259.
  • Lubchenko V, Wolynes PG. Theory of structural glasses and supercooled liquids. Annu Rev Phys Chem. 2007;58:235–266.
  • Vannoni M, Sordini A, Molesini G. Relaxation time and viscosity of fused silica glass at room temperature. Eur Phys J E. 2011;34(9):92.
  • Angell CA. Formation of glasses from liquids and biopolymers. Science. 1995;267(5206):1924–1935.
  • Blodgett M, Egami T, Nussinov Z, et al. Proposal for universality in the viscosity of metallic liquids. Sci Rep. 2015;5:13837.
  • Dyre JC. Colloquium: the glass transition and elastic models of glass-forming liquids. Rev Mod Phys. 2006;78(3):953.
  • Garca-Coln L, Del Castillo L, Goldstein P. Theoretical basis for the Vogel-Fulcher-Tammann equation. Phys Rev B. 1989;40(10):7040.
  • Fulcher GS. Analysis of recent measurements of the viscosity of glasses. J Am Ceram Soc. 1925;8(6):339–355.
  • Cohen MH, Grest G. Liquid-glass transition, a free-volume approach. Phys Rev B. 1979;20(3):1077.
  • Moynihan CT, Mossadegh R, Gupta P, Drexhage MG. Crystallization and viscosity of heavy metal fluoride glasses. In: Moynihan C, Mossadegh R, Gupta P, et al., editor. Infrared optical materials and fibers IV. Vol. 618. Bellingham (WA, USA): International Society for Optics and Photonics; 1986. p. 178–183.
  • Gutzow IS, Mazurin OV, Todorova SV, et al. Glasses and the glass transition. Hoboken (NJ, USA): John Wiley & Sons, Inc.; 2011.
  • Volf MB. Mathematical approach to glass. Amsterdam; New York: Elsevier; 1988. ( Glass science and technology; 9).
  • Fluegel A. Glass viscosity calculation based on a global statistical modelling approach. Glass Technol-Eur J Glass Sci Technol Part A. 2007 Feb;48(1):13–30.
  • Kim C, Pilania G, Ramprasad R. Machine learning assisted predictions of intrinsic dielectric breakdown strength of ABX3 perovskites. J Phys Chem C. 2016;120(27):14575–14580.
  • Ghiringhelli LM, Vybiral J, Levchenko SV, et al. Big data of materials science: critical role of the descriptor. Phys Rev Lett. 2015;114(10):105503.
  • Lorenz S, Groß A, Scheffler M. Representing high-dimensional potential-energy surfaces for reactions at surfaces by neural networks. Chem Phys Lett. 2004;395(4–6):210–215.
  • Behler J, Parrinello M. Generalized neural-network representation of high-dimensional potential-energy surfaces. Phys Rev Lett. 2007;98(14):146401.
  • Bartók AP, Kondor R, Csányi G. On representing chemical environments. Phys Rev B. 2013;87(18):184115.
  • Isayev O, Oses C, Toher C, et al. Universal fragment descriptors for predicting properties of inorganic crystals. Nat Commun. 2017;8:15679.
  • Gossett E, Toher C, Oses C, et al. AFLOW-ML: A RESTful API for machine-learning predictions of materials properties. Comput Mater Sci. 2018;152:134–145.
  • Ong SP, Richards WD, Jain A, et al. Python Materials Genomics (pymatgen): A robust, open-source python library for materials analysis. Comput Mater Sci. 2013;68:314–319.
  • Ward L, Agrawal A, Choudhary A, et al. A general-purpose machine learning framework for predicting properties of inorganic materials. NPJ Comput Mater. 2016;2:16028.
  • Isayev O, Fourches D, Muratov EN, et al. Materials cartography: representing and mining materials space using structural and electronic fingerprints. Chem Mater. 2015;27(3):735–743.
  • Perry RH, Green DW. Perry’s chemical engineers’ handbook. 8th ed. New York: McGraw-Hill; 2008.
  • Shannon RD. Revised effective ionic radii and systematic studies of interatomic distances in halides and chalcogenides. Acta Crystallogr A. 1976;32(5):751–767.
  • MDL Information Systems, Inc. MDL® SciGlass, version 6. San Leandro, CA: MDL Information Systems, Inc.; 2003.
  • New Glass Forum. INTERGLAD International Glass Database. 7 ed. Tokyo, Japan: New Glass Forum; 2016.
  • Priven AI, Mazurin OV. Glass property databases: their history, present state, and prospects for further development. In: Priven A, Mazurin O, editors. Advanced materials research. Vol. 39. Switzerland: Trans Tech Publications; 2008. p. 147–152.
  • Lide DR. CRC handbook of chemistry and physics. Vol. 82. Boca Ration, FL: CRC Press; 2001.
  • Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: Pereira F, Burges CJC, Bottou L, Weinberger KQ, editors. Advances in neural information processing systems. Red Hook (NY, USA): Curran Associates, Inc.; 2012. p. 1097–1105. Available from: http://papers.nips.cc/paper/4824-imagenet-classification-withdeep-convolutional-neural-networks.pdf
  • Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:14091556. 2014.
  • Bahdanau D, Cho K, Bengio Y. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:14090473. 2014.
  • Wu Y, Schuster M, Chen Z, et al. Google’s neural machine translation system: bridging the gap between human and machine translation. arXiv preprint arXiv:160908144. 2016.
  • Silver D, Hubert T, Schrittwieser J, et al. A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science. 2018;362(6419):1140–1144.
  • Nagai R, Akashi R, Sasaki S, et al. Neural-network Kohn-Sham exchange-correlation potential and its out-of-training transferability. J Chem Phys. 2018;148(24):241737.
  • Dreyfus C, Dreyfus G. A machine learning approach to the estimation of the liquidus temperature of glass-forming oxide blends. J Non-Crystalline Solids. 2003;318(1–2):63–78.
  • Cybenko G. Approximation by superpositions of a sigmoidal function. Math Control Signals Syst. 1989;2(4):303–314.
  • Hornik K. Approximation capabilities of multilayer feedforward networks. Neural Networks. 1991;4(2):251–257.
  • Glorot X, Bordes A, Bengio Y, editors. Deep sparse rectifier neural networks. Proceedings of the fourteenth international conference on artificial intelligence and statistics; 2011; Ft. Lauderdale (FL, USA).
  • Hoerl AE, Kennard RW. Ridge regression: biased estimation for nonorthogonal problems. Technometrics. 1970;12(1):55–67.
  • Ng AY, editor. Feature selection, L 1 vs. L 2 regularization, and rotational invariance. Proceedings of the twenty-first international conference on Machine learning. Banff (Alberta, Canada): ACM; 2004.
  • Kingma DP, Ba J. Adam: A method for stochastic optimization. arXiv preprint arXiv:14126980. 2014.
  • Chollet FK. 2015. Available from: https://keras.io
  • Abadi M, Agarwal A, Barham P, et al. Tensorflow: large-scale machine learning on heterogeneous distributed systems. arXiv preprint arXiv:160304467. 2016.
  • Fischler MA, Bolles RC. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM. 1981;24(6):381–395.
  • Choi S, Kim T, Yu W. Performance evaluation of RANSAC family. J Comput Vision. 1997;24(3):271–300.
  • Subbarao R, Meer P, editors. Heteroscedastic projection based M-estimators. 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR’05)-Workshops. San Diego (CA, USA): IEEE; 2005.
  • Pearson K. LIII. On lines and planes of closest fit to systems of points in space. London, Edinburgh, Dublin Philos Mag J Sci. 1901;2(11):559–572.
  • Jolliffe IT, Cadima J. Principal component analysis: a review and recent developments. Philos Trans R Soc A Math Phys Eng Sci. 2016;374(2065):20150202.
  • Bayardo RJ, Ma Y, Srikant R, editors. Scaling up all pairs similarity search. Proceedings of the 16th international conference on World Wide Web; Banff (Alberta, Canada): ACM; 2007.
  • Huang A, editor. Similarity measures for text document clustering. Proceedings of the sixth new zealand computer science research student conference (NZCSRSC2008), Christchurch, New Zealand; 2008.