A machine learning approach to the prediction of the dispersion property of oxide glass

被引:3
作者
Tokuda, Yomei [1 ,3 ]
Fujisawa, Misa [1 ]
Ogawa, Jinto [1 ]
Ueda, Yoshikatsu [2 ]
机构
[1] Shiga Univ, Fac Educ, Shiga, Japan
[2] Kyoto Univ, Res Inst Sustainable Humanophere, Kyoto, Japan
[3] Shiga Univ, 2-5-1 Hiratsu, Otsu, Shiga, Japan
关键词
GAUSSIAN-PROCESSES;
D O I
10.1063/5.0075425
中图分类号
TB3 [工程材料学];
学科分类号
0805 ; 080502 ;
摘要
In this study, we built a model for predicting the optical dispersion property of oxide glasses via machine-learning techniques such as kernel ridge regression, neural networks, and random forests. The models precisely predicted the optical property. Based on the predictions for glasses with doped oxides, we prepared new glasses in our laboratory. The experiments agreed well with the predictions made using kernel ridge regression and neural networks but not with those made using random forests. The results of this study demonstrate that the data-driven approach is a promising route for new material design.
引用
收藏
页数:6
相关论文
共 28 条
[1]   Explainable Machine Learning Algorithms For Predicting Glass Transition Temperatures [J].
Alcobaca, Edesio ;
Mastelini, Saulo Martiello ;
Botari, Tiago ;
Pimentel, Bruno Almeida ;
Cassar, Daniel Roberto ;
de Leon Ferreira de Carvalho, Andre Carlos Ponce ;
Zanotto, Edgar Dutra .
ACTA MATERIALIA, 2020, 188 :92-100
[2]  
Alexandros A., 2004, J STAT SOFTWARE, V11, P1, DOI [10.18637/jss.v011.i09, DOI 10.18637/JSS.V011.I09]
[3]  
[Anonymous], 1991, NEW GLASS FORUM
[4]   Scalable Gaussian processes for predicting the optical, physical, thermal, and mechanical properties of inorganic glasses with large datasets [J].
Bishnoi, Suresh ;
Ravinder, R. ;
Grover, Hargun Singh ;
Kodamana, Hariprasad ;
Krishnan, N. M. Anoop .
MATERIALS ADVANCES, 2021, 2 (01) :477-487
[5]   Predicting Young's modulus of oxide glasses with sparse datasets using machine learning [J].
Bishnoi, Suresh ;
Singh, Sourabh ;
Ravinder, R. ;
Bauchy, Mathieu ;
Gosvami, Nitya Nand ;
Kodamana, Hariprasad ;
Krishnan, N. M. Anoop .
JOURNAL OF NON-CRYSTALLINE SOLIDS, 2019, 524
[6]  
Bishop C. M., 2006, Pattern recognition and machine learning
[7]   Solubility of glasses in the system P2O5-CaO-MgO-Na2O-TiO2:: Experimental and modeling using artificial neural networks [J].
Brauer, Delia S. ;
Ruessel, Christian ;
Kraft, Joerg .
JOURNAL OF NON-CRYSTALLINE SOLIDS, 2007, 353 (03) :263-270
[8]   Random forests [J].
Breiman, L .
MACHINE LEARNING, 2001, 45 (01) :5-32
[9]   ViscNet: Neural network for predicting the fragility index and the temperature-dependency of viscosity [J].
Cassar, Daniel R. .
ACTA MATERIALIA, 2021, 206
[10]   Novel gallate-based oxide and oxyfluoride glasses with wide transparency, high refractive indices, and low dispersions [J].
Chung, Jaeyeop ;
Watanabe, Yasuhiro ;
Yananba, Yutaka ;
Nakatsuka, Yuko ;
Inoue, Hiroyuki .
JOURNAL OF THE AMERICAN CERAMIC SOCIETY, 2020, 103 (01) :167-175