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RESEARCH PAPER · Korean Journal of Chemical Engineering · 2021

Machine learning-based discovery of molecules, crystals, and composites: A perspective review

Sangwon Lee, Ha-Eun Byun, Mujin Cheon, Jihan Kim, Jay H. Lee

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Machine learning-based discovery of molecules, crystals, and composites: A perspective reviewOriginalNew Opportunity: Machine Learning for Polymer Materials Design and Discovery127Uncertainty estimation in deep learning‐based property models: Graph neural networks applied to the critical properties28Recent progress in generative adversarial networks applied to inversely designing inorganic materials: A brief review50Multi-step lookahead Bayesian optimization with active learning using reinforcement learning and its application to data-driven batch-to-batch optimization15Bayesian Optimization for Efficient Separation of Isopropyl Alcohol and Water Mixtures Using Hollow Fiber Membrane Contactors0Non-myopic Bayesian optimization using model-free reinforcement learning and its application to optimization in electrochemistry8An extensive review on lung cancer therapeutics using machine learning techniques: state-of-the-art and perspectives65Sustainable Isopropyl Alcohol Recovery via Data-Driven, Active-Learning Optimization of Vacuum Membrane Distillation8Machine learning applied to the design and optimization of polymeric materials: A review51Novel inverse predictive system integrated with industrial lubricant information3Machine learning-assisted development of gas separation membranes: A review13Active Learning for the Discovery of Binary Intermetallic Compounds as Advanced Interconnects7Data-Driven Approach Considering Imbalance in Data Sets and Experimental Conditions for Exploration of Photocatalysts7Rapid Discovery of Graphene Nanoflakes with Desired Absorption Spectra Using DFT and Bayesian Optimization with Neural Network Kernel6Machine learning-based investigation of δ phase precipitation behavior in IN718 alloy and its influence on tensile properties8Generative Deep Learning for the Inverse Design of Materials1

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  1. New Opportunity: Machine Learning for Polymer Materials Design and Discovery

    Advanced Theory and Simulations · 2022-02-12

    Pengcheng Xu, Huimin Chen, Minjie Li, Wencong Lu

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  2. Uncertainty estimation in deep learning‐based property models: Graph neural networks applied to the critical properties

    AIChE Journal · 2022-03-23

    Adem R.N. Aouichaoui, Seyed Soheil Mansouri, Jens Abildskov, Gürkan Sin

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  3. Recent progress in generative adversarial networks applied to inversely designing inorganic materials: A brief review

    Computational Materials Science · 2022-07-01

    Rahma Jabbar, Rahma Jabbar, Rateb Jabbar, Rateb Jabbar, S. Kamoun

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  4. Multi-step lookahead Bayesian optimization with active learning using reinforcement learning and its application to data-driven batch-to-batch optimization

    Computers & Chemical Engineering · 2022-09-07

    Ha-Eun Byun, Boeun Kim, Jay H. Lee

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  5. Bayesian Optimization for Efficient Separation of Isopropyl Alcohol and Water Mixtures Using Hollow Fiber Membrane Contactors

    SSRN Electronic Journal · 2023-01-01

    Jimin Park, Mujin Cheon, Sanghyeon Park, Jay H. Lee, Dong‐Yeun Koh

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  6. Non-myopic Bayesian optimization using model-free reinforcement learning and its application to optimization in electrochemistry

    Computers & Chemical Engineering · 2024-02-07

    Mujin Cheon, Ha-Eun Byun, Jay H. Lee

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  7. An extensive review on lung cancer therapeutics using machine learning techniques: state-of-the-art and perspectives

    Journal of drug targeting · 2024-04-25

    Shaban Ahmad, Khalid Raza

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  8. Sustainable Isopropyl Alcohol Recovery via Data-Driven, Active-Learning Optimization of Vacuum Membrane Distillation

    ACS Sustainable Chemistry & Engineering · 2024-07-21

    Jimin Park, Mujin Cheon, Sanghyeon Park, Jay H. Lee, Dong‐Yeun Koh

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  9. Machine learning applied to the design and optimization of polymeric materials: A review

    Next Materials · 2024-12-14

    Sudarsan M. Pai, Karim Ali Shah, Sruthi Sunder, Rodrigo Queiroz Albuquerque, Christian Brütting, Holger Ruckdäschel

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  10. 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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  11. Machine learning-assisted development of gas separation membranes: A review

    Carbon Capture Science & Technology · 2025-01-30

    An Li, Jianchun Chu, Shaoxuan Huang, Yongqi Liu, Maogang He, Xiangyang Liu

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  12. Active Learning for the Discovery of Binary Intermetallic Compounds as Advanced Interconnects

    The Journal of Physical Chemistry Letters · 2025-04-02

    Guoxiang Cui, Zikang Guo, Xiangyu Ren, Yuhang Jiang, Xinyu Jin, Yunwen Wu, Shenghong Ju

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  13. Data-Driven Approach Considering Imbalance in Data Sets and Experimental Conditions for Exploration of Photocatalysts

    ACS Omega · 2025-04-10

    Wataru Takahara, Ryuto Baba, Yosuke Harashima, Tomoaki Takayama, Shogo Takasuka, Yuichi Yamaguchi, Akihiko Kudo, Mikiya Fujii

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  14. Rapid Discovery of Graphene Nanoflakes with Desired Absorption Spectra Using DFT and Bayesian Optimization with Neural Network Kernel

    The Journal of Physical Chemistry A · 2025-05-08

    Şener Özönder, Hatice Kübra KÜÇÜKKARTAL

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  15. Machine learning-based investigation of δ phase precipitation behavior in IN718 alloy and its influence on tensile properties

    Materials Science and Engineering A · 2025-08-20

    Zhixuan Cheng, Jianli Zhou, Guanghao Guo, Shuang Chen, Xianjue Ye, Xiao Wei, Yuefei Zhang, Ze Zhang

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  16. Generative Deep Learning for the Inverse Design of Materials

    Machine intelligence for materials science · 2026-01-01

    Yixuan Zhang, Teng Long, Hongbin Zhang

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