Predicting and interpreting oxide glass properties by machine learning using large datasets

Daniel R. Cassar, Saulo Martiello Mastelini, Tiago Botari, Edesio Alcobaça, André C.P.L.F. de Carvalho and Edgar D. Zanotto

Ceramics International, 2021, 47, 23958
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Accelerating design of glass substrates by machine learning using small-to-medium datasets

Jiaqian Zhu, Linfeng Ding, Guohao Sun and Lianjun Wang

Ceramics International, 2024, 50, 3018
DOI: 10.1016/j.ceramint.2023.11.048

Role of HfO2 on Modifying the structural, optical, elastic, and mechanical features of boro tellurite glasses

Sleman Yahya Rasul, Bulent Aktas, Soran Saleem Rustum, Serife Yalcin, Ahmad Fahad, Abuzer Acikgoz and Gokhan Ceyhan

Optical Materials, 2025, 159, 116564
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Machine learning-based model of surface tension of liquid metals: a step in designing multicomponent alloys for additive manufacturing

Mariam Assi, Julien Favre, Anna Fraczkiewicz and Franck Tancret

J Mater Sci, 2022
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Interpretable machine learning for understanding compositional and testing condition effects on refractive index, density, dielectric constant, and loss tangent of inorganic melts and glasses

Mohd Zaki, Jayadeva and N. M. Anoop Krishnan

Front. Mater., 2024, 11
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A machine learning approach to the prediction of the dispersion property of oxide glass

Yomei Tokuda, Misa Fujisawa, Jinto Ogawa and Yoshikatsu Ueda

AIP Advances, 2021, 11
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Hydraulic fracturing performance analysis by the mutual information and Gaussian process regression methods

Xu Yan, Dehua Liu, Wenhua Xu, Denghui He and Haiyang Hao

Engineering Fracture Mechanics, 2023, 286, 109285
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Deep learning-assisted attribute prediction of chalcogenide glasses based on graph classification

Hui Li, Pan Liu, Shaoyun Liu and Bin Yang

Sci Rep, 2025, 15
DOI: 10.1038/s41598-025-04391-9

Machine learning-aided cost prediction and optimization in construction operations

Virok Sharma, Mohd Zaki, Kumar Neeraj Jha and N. M. Anoop Krishnan

ECAM, 2021
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Understanding the compositional control on electrical, mechanical, optical, and physical properties of inorganic glasses with interpretable machine learning

Ravinder Bhattoo, Suresh Bishnoi, Mohd Zaki and N. M. Anoop Krishnan

Acta Materialia, 2023, 242, 118439
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Machine learning unveils composition-property relationships in chalcogenide glasses

Saulo Martiello Mastelini, Daniel R. Cassar, Edesio Alcobaça, Tiago Botari, André C.P.L.F. de Carvalho and Edgar D. Zanotto

Acta Materialia, 2022, 240, 118302
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Data‐driven prediction of room‐temperature density for multicomponent silicate‐based glasses

Kai Gong and Elsa Olivetti

J Am Ceram Soc., 2023, 106, 4142
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Data-augmented machine learning models for oxynitride glasses via Wasserstein generative adversarial network with gradient penalty and content constraint

Jing Tian, Yuan Li, Min Guan, Jijie Zheng, Jingyuan Chu, Yong Liu and Gaorong Han

Journal of Materiomics, 2025, 101111
DOI: 10.1016/j.jmat.2025.101111

Контент-анализ данных о термических свойствах фторидных и модифицированных фторидных стекол

Л. А. Ваймугин, К. С. Никонов, Л. В. Моисеева and М. Н. Бреховских

Neorganičeskie materialy, 2023, 59, 1043
DOI: 10.31857/S0002337X23090154

Glassomics: An omics approach toward understanding glasses through modeling, simulations, and artificial intelligence

Mohd Zaki, Amreen Jan, N. M. Anoop Krishnan and John C. Mauro

MRS Bulletin, 2023, 48, 1026
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Interpretable machine learning methods to predict the mechanical properties of ABX3 perovskites

S.B. Akinpelu, S.A. Abolade, E. Okafor, D.O. Obada, A.M. Ukpong, S. Kumar R., J. Healy and A. Akande

Results in Physics, 2024, 65, 107978
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Content Analysis of Data on the Thermal Properties of Fluoride and Modified Fluoride Glasses

L. A. Vaimugin, K. S. Nikonov, L. V. Moiseeva and M. N. Brekhovskikh

Inorg Mater, 2023, 59, 1002
DOI: 10.1134/S0020168523090157

Extracting processing and testing parameters from materials science literature for improved property prediction of glasses

Mohd Zaki, Jayadeva and N.M. Anoop Krishnan

Chemical Engineering and Processing - Process Intensification, 2022, 180, 108607
DOI: 10.1016/j.cep.2021.108607

Artificial intelligence and machine learning in glass science and technology: 21 challenges for the 21st century

Ravinder, Vineeth Venugopal, Suresh Bishnoi, Sourabh Singh, Mohd Zaki, Hargun Singh Grover, Mathieu Bauchy, Manish Agarwal and N. M. Anoop Krishnan

Int J of Appl Glass Sci, 2021, 12, 277
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Accelerated design of chalcogenide glasses through interpretable machine learning for composition–property relationships

Sayam Singla, Sajid Mannan, Mohd Zaki and N M Anoop Krishnan

J. Phys. Mater., 2023, 6, 024003
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AI-driven design of fluorine-free polymers for sustainable and high-performance anion exchange membranes

William Schertzer, Shivank Shukla, Abhishek Sose, Reanna Rafiq, Mohammed Al Otmi, Janani Sampath, Ryan P. Lively and Rampi Ramprasad

J. Mater. Inf., 2025, 5
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Interpreting the optical properties of oxide glasses with machine learning and Shapely additive explanations

Mohd Zaki, Vineeth Venugopal, Ravinder Bhattoo, Suresh Bishnoi, Sourabh Kumar Singh, Amarnath R. Allu, Jayadeva and N. M. Anoop Krishnan

J Am Ceram Soc., 2022, 105, 4046
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Reconstructing Chromatic-Dispersion Relations and Predicting Refractive Indices Using Text Mining and Machine Learning

Jiuyang Zhao and Jacqueline M. Cole

J. Chem. Inf. Model., 2022, 62, 2670
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Evaluation of principal features for predicting bulk and shear modulus of inorganic solids with machine learning

Myeonghun Lee, Minseon Kim and Kyoungmin Min

Materials Today Communications, 2022, 33, 104208
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Directed Gaussian process metamodeling with improved firefly algorithm (iFA) for composite manufacturing uncertainty propagation analysis

Amit Kumar Ball, Kai Zhou, Dong Xu, Dianyun Zhang and Jiong Tang

Int J Adv Manuf Technol, 2023, 126, 49
DOI: 10.1007/s00170-023-10994-1

Ontology‐Based Digital Infrastructure for Data‐Driven Glass Development

Ya‐Fan Chen, Felix Arendt, Hansjörg Bornhöft, Andréa S. S. de Camargo, Joachim Deubener, Andreas Diegeler, Shravya Gogula, Altair T. Contreras Jaimes, Sebastian Kempf, Martin Kilo, René Limbach, Ralf Müller, Rick Niebergall, Zhiwen Pan, Frank Puppe, Stefan Reinsch, Gerhard Schottner, Simon Stier, Tina Waurischk, Lothar Wondraczek and Marek Sierka

Adv Eng Mater, 2025, 27
DOI: 10.1002/adem.202401560

Looking through glass: Knowledge discovery from materials science literature using natural language processing

Vineeth Venugopal, Sourav Sahoo, Mohd Zaki, Manish Agarwal, Nitya Nand Gosvami and N. M. Anoop Krishnan

Patterns, 2021, 2, 100290
DOI: 10.1016/j.patter.2021.100290

Machine-learning and high-throughput studies for high-entropy materials

E-Wen Huang, Wen-Jay Lee, Sudhanshu Shekhar Singh, Poresh Kumar, Chih-Yu Lee, Tu-Ngoc Lam, Hsu-Hsuan Chin, Bi-Hsuan Lin and Peter K. Liaw

Materials Science and Engineering: R: Reports, 2022, 147, 100645
DOI: 10.1016/j.mser.2021.100645

Predicting oxide glass properties with low complexity neural network and physical and chemical descriptors

Suresh Bishnoi, Skyler Badge, Jayadeva and N.M. Anoop Krishnan

Journal of Non-Crystalline Solids, 2023, 616, 122488
DOI: 10.1016/j.jnoncrysol.2023.122488

COVID-19 diagnosis using state-of-the-art CNN architecture features and Bayesian Optimization

Muhammet Fatih Aslan, Kadir Sabanci, Akif Durdu and Muhammed Fahri Unlersen

Computers in Biology and Medicine, 2022, 142, 105244
DOI: 10.1016/j.compbiomed.2022.105244

Comparison of machine-learning models in predicting the liquidus temperature of aluminum cryolitic bath

K. Betsis, N.E. Karkalos and A. Xenidis

Materials Chemistry and Physics, 2025, 340, 130865
DOI: 10.1016/j.matchemphys.2025.130865

Physics-informed machine learning for Na-Ion conductivity and activation energy

Indrajeet Mandal, Sajid Mannan, Yuanqing Lu, Nitya Nand Gosvami, Lothar Wondraczek and N.M. Anoop Krishnan

Journal of Non-Crystalline Solids, 2025, 657, 123497
DOI: 10.1016/j.jnoncrysol.2025.123497

Development of hybrid models based on deep learning and optimized machine learning algorithms for brain tumor Multi-Classification

Muhammed Celik and Ozkan Inik

Expert Systems with Applications, 2024, 238, 122159
DOI: 10.1016/j.eswa.2023.122159

Aqueous dissolution of Li-Na borosilicates: Insights from machine learning and experiments

Thomas L. Goût, Joseph N.P. Lillington, James Walden, Christina Boukouvala, Emilie Ringe, Mike T. Harrison and Ian Farnan

Journal of Non-Crystalline Solids, 2023, 621, 122630
DOI: 10.1016/j.jnoncrysol.2023.122630

Machine Learning-Assisted Design of Na-Ion-Conducting Glasses

Indrajeet Mandal, Sajid Mannan, Lothar Wondraczek, Nitya Nand Gosvami, Amarnath R. Allu and N. M. Anoop Krishnan

J. Phys. Chem. C, 2023, 127, 14636
DOI: 10.1021/acs.jpcc.3c01834

GlassNet: A multitask deep neural network for predicting many glass properties

Daniel R. Cassar

Ceramics International, 2023, 49, 36013
DOI: 10.1016/j.ceramint.2023.08.281

Natural language processing-guided meta-analysis and structure factor database extraction from glass literature

Mohd Zaki, Sahith Reddy Namireddy, Tanu Pittie, Vaibhav Bihani, Shweta Rani Keshri, Vineeth Venugopal, Nitya Nand Gosvami, Jayadeva and N.M. Anoop Krishnan

Journal of Non-Crystalline Solids: X, 2022, 15, 100103
DOI: 10.1016/j.nocx.2022.100103

Predicting mechanical behavior of different thin-walled tubes using data-driven models

Hamdi Kuleyi̇n, Yunus Emre Karabacak and Recep Gümrük

Materials Today Communications, 2024, 40, 109998
DOI: 10.1016/j.mtcomm.2024.109998

Prediction of Thermal and Optical Properties of Oxyfluoride Glasses Based on Interpretable Machine Learning

Yuhao Xie and Xiangfu Wang

Nanomaterials, 2025, 15, 860
DOI: 10.3390/nano15110860