Designing high-performance ion-exchangeable glasses with multi-objective optimization and machine learning
Bibliographic record
Abstract
No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.
Source: Not provided · Quality: missing
Research context
OpenAlex topics: Glass properties and applications · Luminescence Properties of Advanced Materials · Pigment Synthesis and Properties
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (70)
Show source-provided references
1. Chapter 18 - strength and toughness
2. Varshneya · 2015
3. Varshneya · 2012
4. Electric field‐assisted ion exchange strengthening of borosilicate and soda lime silicate glass10.1111/ijag.12266 ↗
5. Stress buildup and relaxation during ion exchange strengthening of glass10.1111/j.1151-2916.1987.tb04934.x ↗
6. Strengthening of class fibers: 11, ion exchange10.1111/j.1151-2916.1969.tb16073.x ↗
7. Chapter 12 - diffusion in polymers and glasses
8. Chapter 6 - chemical strengthening of glass
9. Electric-field assisted ion-exchange of innovative float glass10.1016/j.jnoncrysol.2022.121994 ↗
10. Electric field-assisted ion exchange strengthening of borosilicate and soda lime silicate glass10.1111/ijag.12266 ↗
11. Buildup and relaxation of stress in chemically strengthened glass10.1016/j.jnoncrysol.2015.07.037 ↗
12. Ultra-thin glass as a substrate for flexible photonics10.1016/j.optmat.2020.109994 ↗
13. 2015
14. Thermal treatment of ion-exchanged glass10.1111/ijag.16590 ↗
15. Structure and mechanical response of chemically strengthened aluminosilicate glass under different post-annealing conditions10.1016/j.jnoncrysol.2020.120620 ↗
16. Can annealing improve the chemical strengthening of thin borosilicate glass?10.1016/j.jnoncrysol.2017.03.038 ↗
17. Hardness and scratch resistance of chemically strengthened alkali-borosilicate thin glass10.1111/jace.19804 ↗
18. Characterisation of the scratch resistance of annealed and tempered architectural glass10.1016/j.tsf.2011.04.104 ↗
19. Surface hardness and abrasion threshold of chemically strengthened soda-lime silicate glasses after steam processing10.52825/glass-europe.v1i.435 ↗
20. Surface damage resistance and yielding of chemically strengthened silicate glasses: from normal indentation to scratch loading10.1111/jace.17758 ↗
21. A comparative study on the effect of loading speed and surface scratches on the flexural strength of aluminosilicate glass: annealed vs. chemically strengthened10.1016/j.ceramint.2018.03.166 ↗
22. Predicting the flexural strength of chemically strengthened aluminosilicate glass plates with fractography10.1016/j.jeurceramsoc.2022.03.007 ↗
23. Investigation of ion-exchange ‘stuffed’ glass structures by molecular dynamics simulation10.1016/j.jnoncrysol.2012.05.025 ↗
24. Molecular dynamics simulations of ion-exchanged glass10.1016/j.jnoncrysol.2014.07.025 ↗
25. Simulation of glass network evolution during chemical strengthening: resolution of the subsurface compression maximum anomaly10.1016/j.jnoncrysol.2019.05.033 ↗
26. Entropy production during ion exchange of glass10.1111/jace.19534 ↗
27. Experimental inquiry and machine learning for predictive analysis of friction stir welded AA5052 and AZ31B dissimilar joints
28. Mechanical and compositional design of high-strength Corning Gorilla® Glass
29. Predicting the dissolution kinetics of silicate glasses using machine learning10.1016/j.jnoncrysol.2018.02.023 ↗
30. Predicting glass transition temperatures using neural networks10.1016/j.actamat.2018.08.022 ↗
31. Machine learning unveils composition-property relationships in chalcogenide glasses10.1016/j.actamat.2022.118302 ↗
32. Glass hardness: predicting composition and load effects via symbolic reasoning-informed machine learning10.1016/j.actamat.2023.119046 ↗
33. Understanding the compositional control on electrical, mechanical, optical, and physical properties of inorganic glasses with interpretable machine learning10.1016/j.actamat.2022.118439 ↗
34. Deep learning aided rational design of oxide glasses10.1039/D0MH00162G ↗
35. Predicting and interpreting oxide glass properties by machine learning using large datasets10.1016/j.ceramint.2021.05.105 ↗
36. Machine learning versus human learning in predicting glass-forming ability of metallic glasses10.1016/j.actamat.2022.118497 ↗
37. Cassar · 2023
38. Predicting the Young's modulus of silicate glasses using high-throughput molecular dynamics simulations and machine learning10.1038/s41598-019-45344-3 ↗
39. Thermodynamic parameters of bonds in glassy materials from viscosity–temperature relationships10.1088/0953-8984/19/41/415107 ↗
40. Designing optical glasses by machine learning coupled with a genetic algorithm10.1016/j.ceramint.2020.12.167 ↗
41. Commercial glass strengthening and safety technologies: lessons learned and yet to be learned10.13036/17533546.65.3.06 ↗
42. A fast and elitist multiobjective genetic algorithm: nsga-II10.1109/4235.996017 ↗
43. Estimating “depth of layer” (DOL) in ion-exchanged glasses using explainable machine learning10.1016/j.mtla.2024.102027 ↗
44. Solving large scale linear prediction problems using stochastic gradient descent algorithms
45. Scikit-learn: machine learning in Python
46. Random search for hyper-parameter optimization
47. Bagging predictors10.1007/BF00058655 ↗
48. Nearest neighbor pattern classification10.1109/TIT.1967.1053964 ↗
49. Corn: correlation-driven nonparametric learning approach for portfolio selection10.1145/1961189.1961193 ↗
50. A. Guryanov, "Histogram-based algorithm for building gradient boosting ensembles of piecewise linear decision trees," in Analysis Of Images, Social Networks And Texts: 8th International Conference, AIST 2019, Kazan, Russia, July 17–19, 2019, Revised Selected Papers 8, 2019: Springer, pp. 39-50.10.1007/978-3-030-37334-4_4 ↗
51. Goodfellow · 2016
52. Multiobjective evolutionary algorithm with controllable focus on the knees of the Pareto front10.1109/TEVC.2009.2017515 ↗
53. From local explanations to global understanding with explainable AI for trees10.1038/s42256-019-0138-9 ↗
54. A unified approach to interpreting model predictions
55. O. Banapour Ghaffari, B. Eftekhari Yekta, M. Zakeri-Nasrabadi, . Ion Exchangeable Glasses Dataset, doi: https://doi.org/10.5281/zenodo.10894643.
56. Mechanistic understanding of the fracture toughening in chemically strengthened glass—experiments and phase-field fracture modeling10.1016/j.ijsolstr.2021.111374 ↗
57. A multi-step chemical ion-exchange process for alkali/alkaline-earth aluminosilicate glasses10.1016/j.ceramint.2019.09.050 ↗
58. Strengthening by ion exchange10.1111/j.1151-2916.1964.tb14399.x ↗
59. Ion exchange for glass strengthening10.1016/j.mseb.2007.11.029 ↗
60. Effect of B2O3 substitution for SiO2 in alkali aluminoborosilicate glasses on chemical strengthening10.2109/jcersj2.19174 ↗
61. Direct calculation of Young's moidulus of glass10.1016/0022-3093(73)90053-7 ↗
62. Effect of ZrO2 crystallization on ion exchange properties in aluminosilicate glass10.1016/j.jeurceramsoc.2020.01.001 ↗
63. BaO-doped silicate and borosilicate glasses for enhanced chemical durability: molecular dynamics simulations based strategy for glass design10.1039/D2ME00094F ↗
64. Principal component analysis10.1016/0169-7439(87)80084-9 ↗
65. Principal component analysis10.1038/s43586-022-00192-w ↗
66. Effect of Al2O3 content on the mechanical and interdiffusional properties of ion-exchanged Na-aluminosilicate glasses10.1016/j.jnoncrysol.2016.12.019 ↗
67. The effect of substitution of Al2O3 and B2O3 for SiO2 on the properties of cover glass for liquid crystal display: structure, thermal, visco-elastic, and physical properties10.1111/ijag.15904 ↗
68. Calculation of activation energy of ionic conductivity in silica glasses by classical methods10.1111/j.1151-2916.1954.tb13991.x ↗
69. Effect of TiO2 addition on the properties of complex aluminosilicate glasses and glass-ceramics10.1016/S0025-5408(97)00029-9 ↗
70. Alkali ion diffusion and structure of chemically strengthened TiO2 doped soda-lime silicate glass10.1016/j.jnoncrysol.2022.121564 ↗