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RESEARCH PAPER · Faraday Discussions · 2024

A critical reflection on attempts to machine-learn materials synthesis insights from text-mined literature recipes

Wenhao Q. Sun, Nicholas David

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16 direct citing papers in this saved network. Explore the next layer with second-order citations.

Open in your workspace →Saved 2026-10-14 · complete coverage
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A critical reflection on attempts to machine-learn materials synthesis insights from text-mined literature recipesOriginalThe amorphous state as a frontier in computational materials design74Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models28Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models1Foundation models for materials discovery – current state and future directions102Machine learning approaches for intentional materials engineering5Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook10A quantitative criterion for evidencing predicted compounds in high-throughput powder X-ray diffraction data: Illustration with half-antiperovskite0Advancing materials discovery through artificial intelligence68Data Driven Band Gap Prediction of Oxides for Sustainable Energy Applications0Language Models Enable Data-Augmented Synthesis Planning for Inorganic Materials8Text-mined dataset of solid-state syntheses with impurity phases using Large Language Model6Redefining "Reproducibility" in Data-Driven Materials Synthesis: Between Algorithmic and Experimental Replication0Artificial Intelligence in Accelerating Materials Discovery: Opportunities and Challenges9Closed‐Loop Solid‐State Synthesis Planning for Materials Discovery With Large Language Models1From Prediction to Physical Meaning in Materials AI0FastNano Liquid: An Automated Platform for Small‐Angle X‐ray Scattering‐Based Materials Discovery0

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16 direct citing papers saved.

  1. The amorphous state as a frontier in computational materials design

    Nature Reviews Materials · 2024-12-16

    Yuanbin Liu, Ata Madanchi, Andy S. Anker, Lena Simine, Volker L. Deringer

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  2. Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models

    Angewandte Chemie International Edition · 2025-02-13

    Seong-Min Kim, Joshua Schrier, Yousung Jung

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  3. Explainable Synthesizability Prediction of Inorganic Crystal Polymorphs Using Large Language Models

    Angewandte Chemie · 2025-02-13

    Seong-Min Kim, Joshua Schrier, Yousung Jung

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  4. Foundation models for materials discovery – current state and future directions

    npj Computational Materials · 2025-03-05

    Edward O. Pyzer‐Knapp, Matteo Manica, Peter Staar, Lucas Morin, Patrick Ruch, Teodoro Laino, John R. Smith, Alessandro Curioni

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  5. Machine learning approaches for intentional materials engineering

    MRS Bulletin · 2025-05-01

    Yu‐chen Karen Chen‐Wiegart, Norbert W. Huber, Kevin G. Yager

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  6. Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

    Advanced Intelligent Discovery · 2025-07-22

    Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai

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  7. A quantitative criterion for evidencing predicted compounds in high-throughput powder X-ray diffraction data: Illustration with half-antiperovskite

    Acta Materialia · 2025-09-17

    Kyoka Nagashima, P. Klavins, Valentin Taufour

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  8. Advancing materials discovery through artificial intelligence

    Applied Materials Today · 2025-11-05

    Martin Otyepka, Martin Pykal, Michal Otyepka

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  9. Data Driven Band Gap Prediction of Oxides for Sustainable Energy Applications

    Asia Meeting on Environment and Electrical Engineering (EEE-AM) · 2025-11-05

    Anh Duc To, Duy Phong Pham

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  10. Language Models Enable Data-Augmented Synthesis Planning for Inorganic Materials

    ACS Applied Materials & Interfaces · 2025-11-26

    Thorben Prein, Elton Pan, Janik Jehkul, Steffen Weinmann, Elsa Olivetti, Jennifer L. M. Rupp

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  11. Text-mined dataset of solid-state syntheses with impurity phases using Large Language Model

    Scientific Data · 2025-12-16

    Sanghoon Lee, Kevin Cruse, Viktoriia Baibakova, Gerbrand Ceder, Anubhav Jain

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  12. Redefining "Reproducibility" in Data-Driven Materials Synthesis: Between Algorithmic and Experimental Replication

    Journal of Computational and Data-Driven Materials Engineering · 2026-01-01

    Fatima Al-Zahra, Amina El Idrissi

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  13. Artificial Intelligence in Accelerating Materials Discovery: Opportunities and Challenges

    ChemistrySelect · 2026-02-01

    Avadhesh Kumar Yadav

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  14. Closed‐Loop Solid‐State Synthesis Planning for Materials Discovery With Large Language Models

    Advanced Materials · 2026-08-10

    Dong Won Jeon, Dong Hwi Kim, Taeyang Jeon, Joo Yeong Na, Heegyu Kim, Hyunsouk Cho, Dae Soo Jung, Ju Li, Jin‐Sung Park, Sung Beom Cho

    Read original paper ↗ ·

  15. From Prediction to Physical Meaning in Materials AI

    ACS Materials Au · 2026-09-01

    Meng Li, Pengzhen Lin

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  16. FastNano Liquid: An Automated Platform for Small‐Angle X‐ray Scattering‐Based Materials Discovery

    Advanced Engineering Materials · 2026-09-14

    Pierre‐Baptiste Flandrin, Émeline Cournède, Jens Krarup, Michael Lesa, Foyzul Hasan, Amandine Séné, Arnaud Lahouati, David Benhaiem, Jérémie-Luc Sanchez, Simon Delacroix, Thierry Gacoin, Fabienne Testard, Damien Faivre, David Carrière, Corinne Chevallard, Olivier Taché, Mark A. Levenstein

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