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RESEARCH PAPER · Advanced Functional Materials · 2023

Functional Material Systems Enabled by Automated Data Extraction and Machine Learning

Payam Kalhor, Nicole Jung, Stefan Bräse, Christof Wöll, Manuel M. Tsotsalas, Pascal Friederich

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Functional Material Systems Enabled by Automated Data Extraction and Machine LearningOriginalUtilizing machine learning to optimize metal–organic framework-derived polymer membranes for gas separation24Digital chemistry: navigating the confluence of computation and experimentation – definition, status quo, and future perspective11Progress toward the computational discovery of new metal–organic framework adsorbents for energy applications203When Machine Learning Meets 2D Materials: A Review156New roles of research data infrastructure in research paradigm evolution7From Deep Learning to ChatGPT for Materials Design2Machine learning assisted mechanical properties prediction of fine denier polyester fiber5Active learning driven discovery of novel alloyed catalysts for selective ammonia oxidation12Synergistic sorption strategies: Ionic liquids-modified MOF matrices for adsorption processes42The master key: structural science in unlocking functional materials advancements5Enhancing the Quality of MOF Thin Films for Device Integration Through Machine Learning: A Case Study on HKUST‐1 SURMOF Optimization20Machine Learning-Based Process Optimization in Biopolymer Manufacturing: A Review62Data‐Driven Innovation in Metal‐Organic Frameworks Photocatalysis: Bridging Gaps for CO 2 Capture and Conversion with FAIR Principles4An explainable “family bucket” model for simultaneous prediction of K-edge XANES for multiple light transition metals3Machine learning-driven molecular generation for accelerated screening of high-performance flame retardants in epoxy resin composites13Artificial Intelligence Paradigms for Next-Generation Metal–Organic Framework Research54High-Performance Polyimide Design: Machine Learning–Accelerated Multiobjective Optimization6Machine learning in gas separation applications0A survey of AI-supported materials informatics7Deep learning methods for 2D material electronic properties9A scientist's guide to AI-driven molecular discovery3AI-powered open-source infrastructure for accelerating materials discovery and advanced manufacturing27Organic and Metallacages for Chemical Sensing: Progress in Water‐Compatible Systems0Machine Learning Optimization of Laser Ablation in Liquid for the Green and Low-Cost Synthesis of Clean Gold Nanoparticles7Harnessing large language models driven literature mining and closed-loop design for all-inorganic perovskites0From Trial‐and‐Error to Artificial Intelligence‐Guided Design of Hard Carbon for Sodium‐Ion Batteries1Data Management and Analysis of Metal–Organic Framework Synthesis Using Data Models1Data-driven prediction and non-compensatory assessment of flame retardancy in phosphorus-containing flame-retardant epoxy resin1Robust flexible sensors, wearable technologies, and artificial intelligence: Elastomeric composite prospects1Information extraction for materials science using large language models: A survey of recent advances and new frontiers1Machine learning-guided design and strain engineering of lead-free double perovskites for enhanced optoelectronic performance1Data-driven and intelligent design of high-entropy alloys: A review of machine learning across composition, microstructure, and properties0Machine Learning‐Triggered Auto‐Discovery and Promotion of Luminescent Materials: From Predictive Modeling to Autonomous Laboratories0

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  1. Utilizing machine learning to optimize metal–organic framework-derived polymer membranes for gas separation

    Journal of Materials Chemistry A · 2023-01-01

    Lena Pilz, Carsten Natzeck, Jonas Wohlgemuth, Nina Scheuermann, Simon Spiegel, Simon Oßwald, Alexander Knebel, Stefan Bräse, Christof Wöll, Manuel M. Tsotsalas, Nicholaus Prasetya

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  2. Digital chemistry: navigating the confluence of computation and experimentation – definition, status quo, and future perspective

    Digital Discovery · 2024-01-01

    Stefan Bräse

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  3. Progress toward the computational discovery of new metal–organic framework adsorbents for energy applications

    Nature Energy · 2024-01-09

    Peyman Z. Moghadam, Yongchul G. Chung, Randall Q. Snurr

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  4. When Machine Learning Meets 2D Materials: A Review

    Advanced Science · 2024-01-26

    Bin Lu, Yuze Xia, Yuqian Ren, Xie Miaomiao, Liguo Zhou, Giovanni Vinai, Simon A. Morton, Andrew T. S. Wee, Wilfred G. van der Wiel, Wen Zhang, Ping Kwan Johnny Wong

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  5. New roles of research data infrastructure in research paradigm evolution

    Journal of Data and Information Science · 2024-03-05

    Yizhan Li, Lu Dong, Xiaoxiao Fan, Ren Wei, Shijie Guo, Wenzhen Ma, Zexia Li

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  6. From Deep Learning to ChatGPT for Materials Design

    Unknown journal · 2024-04-04

    Mohammed Mudabbiruddin, Amir A. Mosavi, Felde Imre

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  7. Machine learning assisted mechanical properties prediction of fine denier polyester fiber

    Journal of the Textile Institute · 2024-04-26

    Ruimin Xie, Xiwen He, Yuxiang Liu, Yumei Zhang, Xiaohui Wang, Jinlong Xu, Huaping Wang

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  8. Active learning driven discovery of novel alloyed catalysts for selective ammonia oxidation

    Chemical Engineering Journal · 2024-05-16

    Jiaqiang Yang, Zhaojie Wang, Xiaofeng Zhang, Xiaofei Zhang, Bingbo Niu, Chengduo Wang, Chun Du, Jilin He, Bin Shan, Qingkui Li

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  9. Synergistic sorption strategies: Ionic liquids-modified MOF matrices for adsorption processes

    Separation and Purification Technology · 2024-05-22

    Maryam Chafiq, Siti Fatimah, Abdelkarim Chaouiki, Young Gun Ko

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  10. The master key: structural science in unlocking functional materials advancements

    Journal of Applied Crystallography · 2024-05-24

    S. Suárez

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  11. Enhancing the Quality of MOF Thin Films for Device Integration Through Machine Learning: A Case Study on HKUST‐1 SURMOF Optimization

    Advanced Functional Materials · 2024-06-03

    Lena Pilz, Meike Koenig, Matthias Schwotzer, Hartmut Gliemann, Christof Wöll, Manuel M. Tsotsalas

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  12. Machine Learning-Based Process Optimization in Biopolymer Manufacturing: A Review

    Polymers · 2024-11-29

    Ivan Pavlovich Malashin, Dmitry Martysyuk, В С Тынченко, Andrei Gantimurov, Семиколенов Андрей Владимирович, Vladimir Nelyub, А. С. Бородулин

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  13. Data‐Driven Innovation in Metal‐Organic Frameworks Photocatalysis: Bridging Gaps for CO 2 Capture and Conversion with FAIR Principles

    Advanced Energy and Sustainability Research · 2024-12-30

    Claudia Bizzarri, Manuel M. Tsotsalas

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  14. An explainable “family bucket” model for simultaneous prediction of K-edge XANES for multiple light transition metals

    Chemical Science · 2025-01-01

    Chenyu Huang, Yunjiang Zhang, Shuyuan Li, Huimin Wang, Yaxin Wang, Shihao Wei, Shaorui Sun

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  15. Machine learning-driven molecular generation for accelerated screening of high-performance flame retardants in epoxy resin composites

    Chemical Engineering Journal · 2025-05-19

    Zhongwei Chen, Chunlei He, Kai Wang, Rong Feng, Long Xiang, Ziwei Zuo, Changliang Wang, Xinyuan Yang, Yong Chang Guo, Juncheng Jiang, Yuan Yu

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  16. Artificial Intelligence Paradigms for Next-Generation Metal–Organic Framework Research

    Journal of the American Chemical Society · 2025-06-24

    Aydın Özcan, François‐Xavier Coudert, Sven M. J. Rogge, Greta Heydenrych, Dong Fan, Antonios P. Sarikas, Seda Keskın, Guillaume Maurin, George E. Froudakis, Stefan Wuttke, İlknur Eruçar

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  17. High-Performance Polyimide Design: Machine Learning–Accelerated Multiobjective Optimization

    ACS Applied Materials & Interfaces · 2025-09-01

    Yu Zhang, Tongle Xu, Luling He, MA Wei-bin, Xiong Li, Minjie Li, Guangrui Xu, Wei Lv, Peng Ding

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  18. Machine learning in gas separation applications

    Elsevier eBooks · 2025-09-27

    Kiran Mustafa, Mashallah Rezakazemi, Rao Muhammad Mahtab Mahboob

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  19. A survey of AI-supported materials informatics

    Computer Science Review · 2025-10-17

    Sanjay Chakraborty, Jonas Björk, Martin Dahlqvist, Johanna Rosen, Fredrik Heintz

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  20. Deep learning methods for 2D material electronic properties

    Digital Discovery · 2025-12-09

    Artem Mishchenko, Anupam Bhattacharya, Xiangwen Wang, Henry Kelbrick Pentz, Yihao Wei, Qian Yang

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  21. A scientist's guide to AI-driven molecular discovery

    Artificial Intelligence Chemistry · 2026-01-24

    Jakes Udabe

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  22. AI-powered open-source infrastructure for accelerating materials discovery and advanced manufacturing

    Communications Materials · 2026-02-17

    Mariangeles Salas, Anand Singh, Carlos Pignataro, Lokendra Pal

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  23. Organic and Metallacages for Chemical Sensing: Progress in Water‐Compatible Systems

    Advanced Sensor Research · 2026-03-01

    Rabia Zahid, Martina Viola, Maria Vittoria Balli, Guillermo Moreno‐Alcántar, Pierre Picchetti

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  24. Machine Learning Optimization of Laser Ablation in Liquid for the Green and Low-Cost Synthesis of Clean Gold Nanoparticles

    Journal of the American Chemical Society · 2026-04-02

    Runpeng Miao, Catherine Reffatto, Mattia Cattelan, Rafael Torres–Mendieta, Luca Menilli, Vincenzo Amendola

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  25. Harnessing large language models driven literature mining and closed-loop design for all-inorganic perovskites

    Journal of Alloys and Compounds · 2026-04-30

    Zewei Yang, Yicheng Ma, Hua Liu, Yali Ji

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  26. From Trial‐and‐Error to Artificial Intelligence‐Guided Design of Hard Carbon for Sodium‐Ion Batteries

    Advanced Functional Materials · 2026-05-07

    Qi Wei, Y Z Liu, Jinhao Pan, X Y Liu, C Y Liu, Jiannan Qi, Bin Wang, Han Xiang Hu, Mingbo Wu

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  27. Data Management and Analysis of Metal–Organic Framework Synthesis Using Data Models

    Journal of Chemical Information and Modeling · 2026-05-08

    Felix Neubauer, Kenichi Endo, Frederic Bender, Esengül Çiftçi, Niels Hansen, Simon Krause, Benjamin Uekérmann, Jürgen Pleiss

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  28. Data-driven prediction and non-compensatory assessment of flame retardancy in phosphorus-containing flame-retardant epoxy resin

    Polymer Degradation and Stability · 2026-05-09

    Qiong Tan, De-Yi Wang

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  29. Robust flexible sensors, wearable technologies, and artificial intelligence: Elastomeric composite prospects

    Journal of Alloys and Compounds · 2026-05-24

    Vineet Kumar, Siraj Azam, Dong-Choon Lee, Sang‐Shin Park

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  30. Information extraction for materials science using large language models: A survey of recent advances and new frontiers

    Materials Today Communications · 2026-06-01

    Yutong Duan, Y L Zhang, Runjia Yu, Dongyuan Li, Satoshi Kosugi, Manabu Okumura, Kotaro Funakoshi

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  31. Machine learning-guided design and strain engineering of lead-free double perovskites for enhanced optoelectronic performance

    Materials Science and Engineering B · 2026-06-09

    Ziyi Zhang, Chunlian Xiong, Changcheng Chen, Songya Wang, Yan Cai, Yaxin Xu, Jiangzhou Xie, Xiaoning Guan, Gang Liu, Pengfei Lu

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  32. Data-driven and intelligent design of high-entropy alloys: A review of machine learning across composition, microstructure, and properties

    Metals Advances · 2026-07-30

    Ruixiao Zhang, Biao Chen, Ruihao Yuan, Yi Williams Wang, Jun Wang, Jinshan Li

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  33. Machine Learning‐Triggered Auto‐Discovery and Promotion of Luminescent Materials: From Predictive Modeling to Autonomous Laboratories

    Advanced Optical Materials · 2026-08-01

    Chao He, Shaoan Zhang, Zonglong Guo, Jialong Xu, Shengzhi Sun, Jianrong Qiu, Yang Li

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