Prediction of glass refractive index based on a multi-algorithm stacked integration model
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OpenAlex topics: Surface Roughness and Optical Measurements · Industrial Vision Systems and Defect Detection · Laser Material Processing Techniques
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References (41)
Show source-provided references
1. Prediction and screening of glass properties based on high-throughput molecular dynamics simulations and machine learning10.1016/j.jnoncrysol.2022.121927 ↗
2. Predicting and optimizing the physical and spectroscopic properties of phosphate laser glasses using a phase diagram approach10.1111/jace.19065 ↗
3. Materials design and discovery with high-throughput density functional theory: the open quantum materials database (OQMD)10.1007/s11837-013-0755-4 ↗
4. Performance characteristics of asphalt materials based on molecular dynamics simulation–a review10.1016/j.conbuildmat.2018.09.038 ↗
5. Progress in modeling of glass properties using topological constraint theory10.1111/ijag.15105 ↗
6. Topological design of structures and composite materials with multiobjectives10.1016/j.ijsolstr.2007.03.028 ↗
7. Neural-network-biased genetic algorithms for materials design: evolutionary algorithms that learn10.1021/acscombsci.6b00136 ↗
8. Advancing the prediction of crystalline phases in glass-ceramics via machine learning10.1016/j.jnoncrysol.2024.123251 ↗
9. Predicting the viscosity of basalt melt by data-driven and interpretable machine learning10.1016/j.jnoncrysol.2024.123302 ↗
10. Viscosity and melting temperature prediction of mold fluxes based on explainable machine learning and shapley additive explanations10.1016/j.jnoncrysol.2024.123037 ↗
11. A micromechanical nested machine learning model for characterizing materials behaviors of bulk metallic glasses
12. Interpretable machine learning for understanding compositional and testing condition effects on refractive index, density, dielectric constant, and loss tangent of inorganic melts and glasses10.3389/fmats.2024.1412701 ↗
13. A novel dimensionality reduction method based on flow model10.1016/j.neucom.2024.128066 ↗
14. Interpreting the optical properties of oxide glasses with machine learning and shapely additive explanations10.1111/jace.18345 ↗
15. Predicting and interpreting oxide glass properties by machine learning using large datasets10.1016/j.ceramint.2021.05.105 ↗
16. Understanding the compositional control on electrical, mechanical, optical, and physical properties of inorganic glasses with interpretable machine learning10.1016/j.actamat.2022.118439 ↗
17. Designing optical glasses by machine learning coupled with a genetic algorithm10.1016/j.ceramint.2020.12.167 ↗
18. Predicting glass transition temperatures using neural networks10.1016/j.actamat.2018.08.022 ↗
19. Judd–ofelt parameters prediction of er+ 3 and nd+ 3 doped oxide glasses using machine learning models10.1016/j.ijleo.2023.170946 ↗
20. Theoretical prediction of vickers hardness for oxide glasses: Machine learning model, interpretability analysis, and experimental validation10.1016/j.mtla.2024.102006 ↗
21. Prediction of optical properties of oxide glass combined with autoencoder and machine learning10.1016/j.jnoncrysol.2024.123166 ↗
22. Refractive-index prediction for high-refractive-index optical glasses based on the B_2O_3-La_2O_3-Ta_2O_5-SiO_2 system using machine learning
23. Component correction and category identification for weathered silicate glass with small datasets10.1109/JSEN.2023.3307596 ↗
24. Ensemble learning based on stacking and blending predicts glass forming ability
25. GlassNet: a multitask deep neural network for predicting many glass properties10.1016/j.ceramint.2023.08.281 ↗
26. 110 Years BK7: optical glass type with long tradition and ongoing progress
27. A review of local outlier factor algorithms for outlier detection in big data streams10.3390/bdcc5010001 ↗
28. Montgomery · 2021
29. A random forest guided tour10.1007/s11749-016-0481-7 ↗
30. Chen · 2015
31. Ridge regression10.1002/wics.14 ↗
32. Extremely randomized trees10.1007/s10994-006-6226-1 ↗
33. Prediction of user outage under typhoon disaster based on multi-algorithm stacking integration10.1016/j.ijepes.2021.107123 ↗
34. Application of improved stacking ensemble learning in NIR spectral modeling of corn seed germination rate10.1016/j.chemolab.2023.105020 ↗
35. A stacking ensemble learning model for monthly rainfall prediction in the Taihu Basin, China10.3390/w14030492 ↗
36. An integrated algorithm for short term charging load prediction of electric vehicles based on a more complete feature set10.1007/s42835-024-01979-5 ↗
37. Bayesian optimization
38. Predicting Young’s modulus of oxide glasses with sparse datasets using machine learning10.1016/j.jnoncrysol.2019.119643 ↗
39. Mutual information analysis: a comprehensive study10.1007/s00145-010-9084-8 ↗
40. Pearson’s correlation coefficient
41. On the tractability of SHAP explanations10.1613/jair.1.13283 ↗