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RESEARCH PAPER · Digital Discovery · 2025

Solid-state synthesizability predictions using positive-unlabeled learning from human-curated literature data

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

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Solid-state synthesizability predictions using positive-unlabeled learning from human-curated literature dataOriginalDefining "Novel Material" in Generative Models: A Boundary between Chemical Validity and Synthesizability0Bridging the synthesizability gap in perovskites by combining computations, literature data, and PU learning1Data-driven exploration of AB 2 X 4 (X = O, S, Se, Te) spinel chemical space0Thermodynamic assessment of machine learning models for solid-state synthesis prediction0Towards sustainable exploration of chemical spaces with machine learning0Application of multi-agent in energy materials research0AI-driven multi-property prediction and manufacturability-aware design for electronic materials and devices0DFT-based grand canonical study of the stability of crystalline battery materials under operating conditions0

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8 direct citing papers saved.

  1. Defining "Novel Material" in Generative Models: A Boundary between Chemical Validity and Synthesizability

    Journal of Computational and Data-Driven Materials Engineering · 2025-01-01

    Khaled Mahfouz, Rania Abdelaziz, Sherif Adel, Dina Mostafa, Tamer Nabil, Reem Saad

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  2. Bridging the synthesizability gap in perovskites by combining computations, literature data, and PU learning

    Machine Learning Science and Technology · 2025-11-24

    Rushik Desai, Junyeong Ahn, Alejandro H. Strachan, Arun Mannodi‐Kanakkithodi

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  3. Data-driven exploration of AB 2 X 4 (X = O, S, Se, Te) spinel chemical space

    Digital Discovery · 2026-01-01

    Panyalak Detrattanawichai, Zhenzhu Li, Hyunsoo Park, Kinga O. Mastej, Aron Walsh

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  4. Thermodynamic assessment of machine learning models for solid-state synthesis prediction

    Materials Horizons · 2026-01-01

    Jane Schlesinger, Simon Hjaltason, Nathan J. Szymanski, Christopher J. Bartel

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  5. Towards sustainable exploration of chemical spaces with machine learning

    Digital Discovery · 2026-01-01

    Leonardo Rafael Medrano Sandonas, David Balcells, Tom Barnowsky, Anton S. Bochkarev, Jacqueline Cole, Volker L. Deringer, Werner Dobrautz, Adrian Ehrenhofer, Thorben Frank, Pascal Friederich, Rico Friedrich, Janine George, Luca M. Ghiringhelli, Alejandra Hinostroza Caldas, Veronika Jurásková, Hannes Kneiding, Yury Lysogorskiy, Johannes T. Margraf, Hanna Türk, O. Anatole von Lilienfield, Milica Todorović, Alexandre Tkatchenko, Mariana Rossi, Gianaurelio Cuniberti

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  6. Application of multi-agent in energy materials research

    Chinese Science Bulletin (Chinese Version) · 2026-01-13

    Ji Wang, Haozhe Zhu, Peiyi Li, Chang Liu, Yang Liu, Jiayu Wan

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  7. AI-driven multi-property prediction and manufacturability-aware design for electronic materials and devices

    Materials Today Electronics · 2026-02-21

    Shujian Zhang, Sihan Wu, Hang Su, Yanqiang Han, Jinjin Li

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  8. DFT-based grand canonical study of the stability of crystalline battery materials under operating conditions

    Physical Review Materials · 2026-08-20

    Johannes Döhn, Axel Groß

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