[1] Ballato J, Seddon A, Clare A et al. Future of optical glass education[J]. Optical Materials Express, 12, 2626-2634(2022).

[2] Walasik W, Traoré D, Amavigan A et al. 2-μm narrow linewidth all-fiber DFB fiber Bragg grating lasers for Ho- and Tm-doped fiber-amplifier applications[J]. Journal of Lightwave Technology, 39, 5096-5102(2021).

[3] Pincemin E, Jauffrit J, Disez P Y et al. 12-core erbium/ytterbium-doped fiber amplifier for 200G/400G long-haul, metro-regional, DCI transmission applications with ROADM[C](2021).

[4] Pan L, Ji S Z, Huang W F et al. Joule-level twelve-pass LD end-pumped bonded neodymium glass laser amplifier[J]. Photonics, 8, 96(2021).

[5] Elisa M, Iordache S M, Iordache A M et al. Peculiarities of the structural and optical properties of rare-earth-doped phosphate glasses for temperature sensing applications[J]. Journal of Non-Crystalline Solids, 556, 120569(2021).

[6] Weng K B, Long N B, Guo Y Q et al. Nanocrystallization of α-CsPbI3 perovskite nanocrystals in GeS2-Sb2S3 based chalcogenide glass[J]. Journal of the European Ceramic Society, 40, 4148-4152(2020).

[7] Calle-Vallejo F, Koper M T M. First-principles computational electrochemistry: achievements and challenges[J]. Electrochimica Acta, 84, 3-11(2012).

[8] Schleder G R, Padilha A C M, Acosta C M et al. From DFT to machine learning: recent approaches to materials science-a review[J]. Journal of Physics: Materials, 2, 032001(2019).

[9] Zhou Q H, Lu S H, Wu Y L et al. Property-oriented material design based on a data-driven machine learning technique[J]. The Journal of Physical Chemistry Letters, 11, 3920-3927(2020).

[10] Bishnoi S, Singh S, Ravinder R et al. Predicting Young’s modulus of oxide glasses with sparse datasets using machine learning[J]. Journal of Non-Crystalline Solids, 524, 119643(2019).

[11] Onbaşlı M C, Tandia A, Mauro J C, Andreoni W, Yip S[M]. Mechanical and compositional design of high-strength corning gorilla^® glass, 1997-2019(2020).

[12] Ghorbani A, Askari A, Malekan M et al. Thermodynamically-guided machine learning modelling for predicting the glass-forming ability of bulk metallic glasses[J]. Scientific Reports, 12, 11754(2022).

[13] Mahesh B. Machine learning algorithms-a review[J]. International Journal of Science and Research, 9, 381-386(2020).

[14] Osisanwo F Y, Akinsola J E T, Awodele O et al. Supervised machine learning algorithms: classification and comparison[J]. International Journal of Computer Trends and Technology, 48, 128-138(2017).

[15] Glielmo A, Husic B E, Rodriguez A et al. Unsupervised learning methods for molecular simulation data[J]. Chemical Reviews, 121, 9722-9758(2021).

[16] Hu Y, Bai X, Yang W J et al. Concatenated dynamic reinforcement learning for multi-staged tasks (MST)[C], 781-788(2021).

[17] Pal M, Bharati P. Introduction to correlation and linear regression analysis[M]. Applications of regression techniques, 1-18(2019).

[18] Yang Y M, Sun L L, Guo C R. Aero-material consumption prediction based on linear regression model[J]. Procedia Computer Science, 131, 825-831(2018).

[19] Maulud D, Abdulazeez A M. A review on linear regression comprehensive in machine learning[J]. Journal of Applied Science and Technology Trends, 1, 140-147(2020).

[20] Ranstam J, Cook J A. LASSO regression[J]. British Journal of Surgery, 105, 1348(2018).

[21] Hoerl R W. Ridge regression: a historical context[J]. Technometrics, 62, 420-425(2020).

[22] Katoch S, Chauhan S S, Kumar V. A review on genetic algorithm: past, present, and future[J]. Multimedia Tools and Applications, 80, 8091-8126(2021).

[23] Albadr M A, Tiun S, Ayob M et al. Genetic algorithm based on natural selection theory for optimization problems[J]. Symmetry, 12, 1758(2020).

[24] Lambora A, Gupta K, Chopra K. Genetic algorithm-a literature review[C], 380-384(2019).

[25] Zhang Q M, Yu H Y, Barbiero M et al. Artificial neural networks enabled by nanophotonics[J]. Light: Science & Applications, 8, 42(2019).

[26] Shahid N, Rappon T, Berta W. Applications of artificial neural networks in health care organizational decision-making: a scoping review[J]. PLoS One, 14, e0212356(2019).

[27] Feng J L, Lu S N. Performance analysis of various activation functions in artificial neural networks[J]. Journal of Physics: Conference Series, 1237, 022030(2019).

[28] Runge J, Zmeureanu R. Forecasting energy use in buildings using artificial neural networks: a review[J]. Energies, 12, 3254(2019).

[29] Wu Z H, Pan S R, Chen F W et al. A comprehensive survey on graph neural networks[J]. IEEE Transactions on Neural Networks and Learning Systems, 32, 4-24(2021).

[30] Karamad M, Magar R, Shi Y T et al. Orbital graph convolutional neural network for material property prediction[J]. Physical Review Materials, 4, 093801(2020).

[31] Maurizi M, Gao C, Berto F. Predicting stress, strain and deformation fields in materials and structures with graph neural networks[J]. Scientific Reports, 12, 21834(2022).

[32] Louie S G, Chan Y H, da Jornada F H et al. Discovering and understanding materials through computation[J]. Nature Materials, 20, 728-735(2021).

[33] Urata S, Nakamura N, Aiba K et al. How fluorine minimizes density fluctuations of silica glass: molecular dynamics study with machine-learning assisted force-matching potential[J]. Materials & Design, 197, 109210(2021).

[34] Yang K, Xu X Y, Yang B et al. Predicting the Young’s modulus of silicate glasses using high-throughput molecular dynamics simulations and machine learning[J]. Scientific Reports, 9, 8739(2019).

[35] Hu Y J, Zhao G, Zhang M F et al. Predicting densities and elastic moduli of SiO2-based glasses by machine learning[J]. NPJ Computational Materials, 6, 25(2020).

[36] Liu H, Fu Z P, Li Y P et al. Machine learning forcefield for silicate glasses[EB/OL]. https://arxiv.org/abs/1902.03486

[37] Krishnan N M A, Mangalathu S, Smedskjaer M M et al. Predicting the dissolution kinetics of silicate glasses using machine learning[J]. Journal of Non-Crystalline Solids, 487, 37-45(2018).

[38] Hamilton J P[M]. Corrosion behavior of sodium aluminosilicate glasses and crystals(1999).

[39] Zaki M, Venugopal V, Bhattoo R et al. Interpreting the optical properties of oxide glasses with machine learning and Shapely additive explanations[J]. Journal of the American Ceramic Society, 105, 4046-4057(2022).

[40] Mangalathu S, Hwang S H, Jeon J S. Failure mode and effects analysis of RC members based on machine-learning-based SHapley Additive exPlanations (SHAP) approach[J]. Engineering Structures, 219, 110927(2020).

[41] Dong G P, Wan T Z, Wu M B et al. Recent applications of glass genetic engineering in laser glasses and other advanced optical glasses[J]. Laser & Optoelectronics Progress, 59, 1516002(2022).

[42] Menezes A D, Teixeira E P, Finzer J R D et al. Machine learning-driven development of niobium-containing optical glasses[J]. Research, Society and Development, 11, e13811931290(2022).

[43] Varshneya A K. Review of ‘SciGlass’ database[J]. American Ceramic Society Bulletin, 76, 82-83(1997).

[44] Nishioka T. Glass fact database “interglad”[J]. Ceramics Japan, 28, 755-758(1993).

[45] Cassar D R, Santos G G, Zanotto E D. Designing optical glasses by machine learning coupled with a genetic algorithm[J]. Ceramics International, 47, 10555-10564(2021).

[46] Raposo F. Evaluation of analytical calibration based on least-squares linear regression for instrumental techniques: a tutorial review[J]. TrAC Trends in Analytical Chemistry, 77, 167-185(2016).

[47] Ying X E. An overview of overfitting and its solutions[J]. Journal of Physics: Conference Series, 1168, 022022(2019).

[48] Holzinger A, Kieseberg P, Weippl E et al. Current advances, trends and challenges of machine learning and knowledge extraction: from machine learning to explainable AI[M]. International cross-domain conference for machine learning and knowledge extraction, 11015, 1-8(2018).

[49] Morgan D, Jacobs R. Opportunities and challenges for machine learning in materials science[J]. Annual Review of Materials Research, 50, 71-103(2020).