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RESEARCH PAPER · Scientific Reports · 2020

Dirty engineering data-driven inverse prediction machine learning model

Jin-Woong Lee, Woon Bae Park, Byung Do Lee, Seonghwan Kim, Nam Hoon Goo, Kee‐Sun Sohn

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Dirty engineering data-driven inverse prediction machine learning modelOriginalA data-driven approach to predicting band gap, excitation, and emission energies for Eu 2+ -activated phosphors28A machine-learning-based alloy design platform that enables both forward and inverse predictions for thermo-mechanically controlled processed (TMCP) steel alloys47Machine Learning Framework for Polymer Discovery5From Drug Molecules to Thermoset Shape Memory Polymers: A Machine Learning Approach50Intelligent Design of High Strength and High Conductivity Copper Alloys Using Machine Learning Assisted by Genetic Algorithm9Optimization-Free Inverse Design of High-Dimensional Nanoparticle Electrocatalysts Using Multi-target Machine Learning5Deep learning for predicting the thermomechanical behavior of shape memory polymers26Programmable Density of Laser Additive Manufactured Parts by Considering an Inverse Problem1The impact of domain-driven and data-driven feature selection on the inverse design of nanoparticle catalysts7Bespoke Learning in Static Systems: Application to Learning Sub-surface Material Density Function0Inverse prediction of capacitor multiphysics dynamic parameters using deep generative model0Recent applications of machine learning in alloy design: A review158Data science and material informatics in physical metallurgy and material science: An overview of milestones and limitations32Inverse design machine learning model for metallic glasses with good glass-forming ability and properties12Experimental Design and Modeling for Forward-Inverse Maps1Exploration of high-ductility ternary refractory complex concentrated alloys using first-principles calculations and machine learning7Novel inverse predictive system integrated with industrial lubricant information3Machine Learning‐Driven Discovery of Thermoset Shape Memory Polymers With High Glass Transition Temperature Using Variational Autoencoders10Targeted Chemical Looping Materials Discovery by an Inverse Design7Comparative Analysis of Deep Learning Approaches for Predicting Thermomechanical Behavior of Shape Memory Polymers0Machine learning for obtaining values of thermomechanical processing parameters of high-entropy alloys with desirable strength and ductility13A Graph Neural Network based deep learning framework for predicting the thermomechanical behavior of thermoset shape memory polymers6Conditional diffusion model for inverse prediction of process parameters and dendritic microstructures from mechanical properties3Deep learning approach for predicting the dynamic mechanical properties of epoxy-based shape memory polymers0

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24 direct citing papers saved.

  1. A data-driven approach to predicting band gap, excitation, and emission energies for Eu 2+ -activated phosphors

    Inorganic Chemistry Frontiers · 2021-01-01

    Chaewon Park, Jin-Woong Lee, Minseuk Kim, Byung Do Lee, Satendra Pal Singh, Woon Bae Park, Kee‐Sun Sohn

    Read original paper ↗ ·

  2. A machine-learning-based alloy design platform that enables both forward and inverse predictions for thermo-mechanically controlled processed (TMCP) steel alloys

    Scientific Reports · 2021-05-26

    Jin-Woong Lee, Chaewon Park, Byung Do Lee, Joonseo Park, Nam Hoon Goo, Kee‐Sun Sohn

    Read original paper ↗ ·

  3. Machine Learning Framework for Polymer Discovery

    Elsevier eBooks · 2021-11-12

    Cheng Yan, Guoqiang Li

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  4. From Drug Molecules to Thermoset Shape Memory Polymers: A Machine Learning Approach

    ACS Applied Materials & Interfaces · 2021-12-08

    Cheng Yan, Xiaming Feng, Guoqiang Li

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  5. Intelligent Design of High Strength and High Conductivity Copper Alloys Using Machine Learning Assisted by Genetic Algorithm

    Computers, materials & continua/Computers, materials & continua (Print) · 2022-01-01

    Parth Khandelwal, Harshit Harshit, I. Manna

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  6. Optimization-Free Inverse Design of High-Dimensional Nanoparticle Electrocatalysts Using Multi-target Machine Learning

    Lecture notes in computer science · 2022-01-01

    Sichao Li, Jonathan Y. C. Ting, Amanda Susan Barnard

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  7. Deep learning for predicting the thermomechanical behavior of shape memory polymers

    Polymer · 2022-10-10

    Diego Segura Ibarra, Jacob Mathews, Fan Li, Hongfang Lü, Guoqiang Li, Jinyuan Chen

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  8. Programmable Density of Laser Additive Manufactured Parts by Considering an Inverse Problem

    Materials · 2022-10-12

    Mika León Altmann, Stefan Bosse, Christian Werner, Rainer Fechte‐Heinen, Anastasiya Toenjes

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  9. The impact of domain-driven and data-driven feature selection on the inverse design of nanoparticle catalysts

    Journal of Computational Science · 2022-10-30

    Sichao Li, Jonathan Y. C. Ting, Amanda Susan Barnard

    Read original paper ↗ ·

  10. Bespoke Learning in Static Systems: Application to Learning Sub-surface Material Density Function

    Springer eBooks · 2023-01-01

    Dalia Chakrabarty

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  11. Inverse prediction of capacitor multiphysics dynamic parameters using deep generative model

    2022 IEEE 72nd Electronic Components and Technology Conference (ECTC) · 2023-05-01

    Kart–Leong Lim, Rahul Dutta, Mihai Dragos Rotaru

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  12. Recent applications of machine learning in alloy design: A review

    Materials Science and Engineering R Reports · 2023-06-16

    Mingwei Hu, Qiyang Tan, Ruth Knibbe, Miao Xu, Bin Jiang, Sen Wang, Xue Li, Mingxing Zhang

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  13. Data science and material informatics in physical metallurgy and material science: An overview of milestones and limitations

    Results in Materials · 2023-09-01

    Desmond Edem Primus Klenam, Tabiri Kwayie Asumadu, Mobin Vandadi, Nima Rahbar, Fred McBagonluri, Winston Oluwole Soboyejo

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  14. Inverse design machine learning model for metallic glasses with good glass-forming ability and properties

    Journal of Applied Physics · 2024-01-10

    Kesong Li, M. Z. Li, Weihua Wang

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  15. Experimental Design and Modeling for Forward-Inverse Maps

    Technometrics · 2024-10-16

    Russell R. Barton, Max D. Morris

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  16. Exploration of high-ductility ternary refractory complex concentrated alloys using first-principles calculations and machine learning

    Calphad · 2024-11-21

    Hyo-Sun Jang, Jin-Woong Lee, Byung Do Lee, Kee‐Sun Sohn, Jiwon Park, Chang-Seok Oh

    Read original paper ↗ ·

  17. Novel inverse predictive system integrated with industrial lubricant information

    Engineering Applications of Artificial Intelligence · 2024-12-20

    Minseong Kim, Minseong Kim, Chonghyo Joo, Jongkoo Lim, Seungho Yeom, Il Moon, Meng Qi, Junghwan Kim

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  18. Machine Learning‐Driven Discovery of Thermoset Shape Memory Polymers With High Glass Transition Temperature Using Variational Autoencoders

    Journal of Polymer Science · 2024-12-25

    Amir Teimouri, Guoqiang Li

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  19. Targeted Chemical Looping Materials Discovery by an Inverse Design

    Advanced Intelligent Systems · 2025-01-26

    Brett A. Duell, Ali Ramazani, Sittichai Natesakhawat, Eric J. Popczun, Jonathan W. Lekse, Yuhua Duan

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  20. Comparative Analysis of Deep Learning Approaches for Predicting Thermomechanical Behavior of Shape Memory Polymers

    Proceedings of IEEE Southeastcon · 2025-03-22

    Khan Raqib Mahmud, Lingxiao Wang, Jinyuan Chen, Xiyuan Liu, Sunzid Hassan

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  21. Machine learning for obtaining values of thermomechanical processing parameters of high-entropy alloys with desirable strength and ductility

    Intermetallics · 2025-06-27

    Zolfaghar Ali Akhlaghi, Hamed Shahmir, Ahmad R. Sharafat

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  22. A Graph Neural Network based deep learning framework for predicting the thermomechanical behavior of thermoset shape memory polymers

    Polymer · 2025-07-16

    Khan Raqib Mahmud, Lingxiao Wang, Jinyuan Chen, Sunzid Hassan

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  23. Conditional diffusion model for inverse prediction of process parameters and dendritic microstructures from mechanical properties

    Scientific Reports · 2025-10-23

    Arisa Ikeda, Ryo Higuchi, Tomohiro Yokozeki, Katsuhiro Endo, Yuta Kojima, Misato Suzuki, Mayu Muramatsu

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  24. Deep learning approach for predicting the dynamic mechanical properties of epoxy-based shape memory polymers

    Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering · 2026-08-14

    Avadesh Yadav, Shikhar Mishra, Sourabh Kumar Singh, Mohit Kumar Pandey, Satish Kumar

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