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RESEARCH PAPER · Chemical Engineering Science · 2024

State-of-the-art review on various applications of machine learning techniques in materials science and engineering

Bing Yu, LiQin Zhang, Xiaoxia Ye, JunQi Wu, JunQi Wu, HuaYong Ying, Wei Zhu, Xiaoming Wu, Xiaoming Wu

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State-of-the-art review on various applications of machine learning techniques in materials science and engineeringOriginalGCN-Based Framework for Materials Screening and Phase Identification1Multi-scale revolution of artificial intelligence in chemical industry7203Chapter 9 Predictive precision: AI-enhanced measurement of mechanical properties in nanostructured polymer composites0Hydrogen Energy Storage via Carbon-Based Materials: From Traditional Sorbents to Emerging Architecture Engineering and AI-Driven Optimization28Artificial Intelligence for Materials Discovery, Development, and Optimization171Machine-Learning-Driven Prediction of Formation Energy and Compositional Design for IrRuRhMoW High-Entropy Alloys5Targeted risk prioritization of typical textile pollutants in surface water from machine learning optimized ToxPi framework11Harnessing Machine Learning for EMI Shielding Materials: State-of-the-Art Advances and Prospects Ahead0Immittance-based machine learning framework for breath biomarker detection using Pd-In2O3-decorated MXene sensors2Machine learning-based prediction and optimization of performances of porous ZrO2/Al2O3 ceramics: a few-shot learning approach1Optimizing Machine Learning Techniques for Precise Prediction of Optical Properties in Silicon Waveguides0Prediction Technique for Heat Transfer in Helically Coiled Tubes via Artificial Neural Networks0Machine learning prediction of failure load in composite and metallic single-lap adhesive joints3Generative AI as an Enabler for Process Intensification3A novel physics-informed neural network architecture for solving nonlinear PDEs in fluid mechanics without labeled data3Machine learning applications in metallic materials: Recent advances and future perspectives8Disaster assessment of oil and gas supply chain based on data-driven dynamic Bayesian networks0Neural network-enabled prediction and optimization of mechanical properties and biocompatibility in high-entropy alloys: A data-driven approach to metallurgical innovation4Designing high energy density materials: A machine learning and DFT approach to optimize detonation velocity and ease of synthesis0Improving Pore Structure Characterization on Hybrid Deep Learning Modeling with Sparse-Label Propagation for Enhancing CO 2 Sequestration Using Well Log Data0Deciphering multiparameter effects on tribological behavior of WC/a-C films through orthogonal design and machine learning0Combining DFT calculations and machine learning for rational design of hydrogen electrocatalysts2Rapid Inverse GISAXS Analysis of Nanoparticle Assemblies with Simulation-Trained Deep Learning0Physics-informed jitter augmentation for hydrodynamic time series0Photocatalytic water splitting: from fundamental mechanisms to practical challenges1Application of Voting Ensemble Machine Learning in Road Lighting Design and the Prediction of Performance Parameters0

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  1. GCN-Based Framework for Materials Screening and Phase Identification

    Materials · 2025-02-21

    Zhenkai Qin, Qining Luo, Weiqi Qin, Xiaolong Chen, Hongfeng Zhang, Cora Un In Wong

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  2. Multi-scale revolution of artificial intelligence in chemical industry

    Frontiers of Chemical Science and Engineering · 2025-05-23

    Ying Li, Quanhu Sun, Zutao Zhu, Huaqiang Wen, Saimeng Jin, Xiangping Zhang, Zhigang Lei, Weifeng Shen

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  3. 203Chapter 9 Predictive precision: AI-enhanced measurement of mechanical properties in nanostructured polymer composites

    De Gruyter eBooks · 2025-06-25

    Kanaka Durga Devi Nelluri, Sk. Abdul Rahaman, Minakshi Kommoju, Bhavana Alaparthi

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  4. Hydrogen Energy Storage via Carbon-Based Materials: From Traditional Sorbents to Emerging Architecture Engineering and AI-Driven Optimization

    Energies · 2025-07-24

    Han Fu, Amin Mojiri, Junli Wang, Zhe Qian Zhao

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  5. Artificial Intelligence for Materials Discovery, Development, and Optimization

    ACS Nano · 2025-07-25

    Benediktus Madika, Aditi Saha, Chaeyul Kang, Batzorig Buyantogtokh, Joshua Agar, Chris M. Wolverton, Peter W. Voorhees, P. B. Littlewood, Sergei V. Kalinin, Seungbum Hong

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  6. Machine-Learning-Driven Prediction of Formation Energy and Compositional Design for IrRuRhMoW High-Entropy Alloys

    Langmuir · 2025-08-08

    Yihao Zheng, Xiangcui Qiu, Haibo Li, Konggang Qu, Hui Yan, Rui Li

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  7. Targeted risk prioritization of typical textile pollutants in surface water from machine learning optimized ToxPi framework

    Journal of environmental chemical engineering · 2025-08-23

    Zhengkun Tang, Chenye Xu, Chensi Shen, Xiang‐Zhou Meng, Fang Li

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  8. Harnessing Machine Learning for EMI Shielding Materials: State-of-the-Art Advances and Prospects Ahead

    International Conference on Computational Intelligence and Knowledge Economy (ICCIKE) · 2025-11-27

    Reenu Elizabeth John, Suresh Babu M, Aagii Mariam Thomas, Prajina O. K, Anusree P G, Steffy Sara Varghese

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  9. Immittance-based machine learning framework for breath biomarker detection using Pd-In2O3-decorated MXene sensors

    Sensors and Actuators B Chemical · 2025-12-03

    Useong Jeong, Myung Hyun Kang, Huisu Shin, Dong-Uk Seo, Don-Kyu Kim, Heesu Hwang, Myeong-Ill Lee, Dokyun Kim, Youkeun K. Oh, Hyoungchul Kim, Sung Myung, Jin‐Ha Hwang

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  10. Machine learning-based prediction and optimization of performances of porous ZrO2/Al2O3 ceramics: a few-shot learning approach

    Applied Materials Today · 2025-12-12

    An Huang, Kai Tan, Xiulan He, Miao Wang, Mingyue Zhou

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  11. Optimizing Machine Learning Techniques for Precise Prediction of Optical Properties in Silicon Waveguides

    Lecture notes in electrical engineering · 2026-01-01

    Mohamed Mammeri, Babak Hashemi, Teresa Crisci, Maurizio Casalino, Francesco Giuseppe Della Corte

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  12. Prediction Technique for Heat Transfer in Helically Coiled Tubes via Artificial Neural Networks

    Springer proceedings in physics · 2026-01-01

    Guitao Yang, Wei Zhang, Bo Yuan, Junsen Fu, Yao Xiao, Hanyang Gu

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  13. Machine learning prediction of failure load in composite and metallic single-lap adhesive joints

    International Journal of Adhesion and Adhesives · 2026-01-08

    Bahador Bahrami, Saba abbaszadeh, Hossein Talebi, Majid Reza Ayatollahi, Mohammad Reza Khosravani

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  14. Generative AI as an Enabler for Process Intensification

    Industrial & Engineering Chemistry Research · 2026-02-09

    Alejandro Montesinos‐Castellanos, Antonio Flores‐Tlacuahuac, Enrique A. López-Guajardo, Karla M. Esquivel-Ortiz, Rubén Morales-Menéndez

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  15. A novel physics-informed neural network architecture for solving nonlinear PDEs in fluid mechanics without labeled data

    Engineering With Computers · 2026-02-17

    Hossein Vasheghani Farahani, Amir Mahdi Tahsini, Ali Nematollahi, Milad Ghiasi Afzal

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  16. Machine learning applications in metallic materials: Recent advances and future perspectives

    Journal of Alloys and Compounds · 2026-03-24

    Bin Miao, Guan-Qi Lin, Yuan Zhang, Yu-Wei Cheng, Huan Yang

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  17. Disaster assessment of oil and gas supply chain based on data-driven dynamic Bayesian networks

    International Journal of Production Research · 2026-04-01

    Wei Xie, Zhenzhong Guan

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  18. Neural network-enabled prediction and optimization of mechanical properties and biocompatibility in high-entropy alloys: A data-driven approach to metallurgical innovation

    Journal of Materials Research and Technology · 2026-04-04

    Sheetal Kumar Dewangan, Sandeep Jain, Hansung Lee, Gyosik Youn, Reliance Jain, Vivek Singh Baghel, Man Mohan, Sourabh Kumar Soni, Manoj S. Choudhari, Rakesh Arya, Byungmin Ahn

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  19. Designing high energy density materials: A machine learning and DFT approach to optimize detonation velocity and ease of synthesis

    Physica B Condensed Matter · 2026-05-05

    Mamduh J. Aljaafreh, M. M. Rashed, Sadaf Noreen

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  20. Improving Pore Structure Characterization on Hybrid Deep Learning Modeling with Sparse-Label Propagation for Enhancing CO 2 Sequestration Using Well Log Data

    Energy & Fuels · 2026-05-25

    Elieneza Nicodemus Abelly, Feng Yang, Samwel Daud Lupyana, Martin Kawamala

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  21. Deciphering multiparameter effects on tribological behavior of WC/a-C films through orthogonal design and machine learning

    Ceramics International · 2026-06-04

    Dongqing He, Bangxin Zhan, Xianzheng Pan, Lunlin Shang, Wensheng Li, Gang Ma

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  22. Combining DFT calculations and machine learning for rational design of hydrogen electrocatalysts

    Chinese Journal of Chemical Engineering · 2026-06-09

    Shuna Yang, HJ Xu, Wenhao Lv, Shaojie Yang, Yawei Liu, Feng Huo

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  23. Rapid Inverse GISAXS Analysis of Nanoparticle Assemblies with Simulation-Trained Deep Learning

    Photon Science · 2026-06-25

    Yufeng Zhai, Jungui Zhou, Shachar Dan, Shouzheng Chen, Julian Eliah Heger, Benedikt Sochor, Arno Jeromin, Wenbo Wang, Wolfgang Johann Parak, Sarathlal Koyiloth Vayalil, Thomas Florian Keller, Andreas Stierle, Alexander Hexemer, Peter Müller‐Buschbaum, Stephan Volkher Roth

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  24. Physics-informed jitter augmentation for hydrodynamic time series

    Journal of Marine Science and Technology · 2026-07-04

    Hamed Majidiyan, Hossein Enshaei, Damon Howe

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  25. Photocatalytic water splitting: from fundamental mechanisms to practical challenges

    International Journal of Hydrogen Energy · 2026-07-11

    Ekaterina V. Sukhanova, Zakhar I. Popov

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  26. Application of Voting Ensemble Machine Learning in Road Lighting Design and the Prediction of Performance Parameters

    LEUKOS The Journal of the Illuminating Engineering Society of North America · 2026-07-15

    Rumpa Saha, Khondekar Lutful Hassan, Moinuddin, Pallav Dutta, Sourin Bhattacharya

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