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RESEARCH PAPER · Digital Discovery · 2025

SynCoTrain: a dual classifier PU-learning framework for synthesizability prediction

Sasan Amariamir, Janine George, Philipp Benner

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9 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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SynCoTrain: a dual classifier PU-learning framework for synthesizability predictionOriginalBridging 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 learning0Bridging theory and experiment in materials discovery: Machine-learning-assisted prediction of synthesizable structures3Anomaly-Detection-Driven Screening of Thermodynamic Stability from Composition Descriptors Alone0AI-driven multi-property prediction and manufacturability-aware design for electronic materials and devices0Yield Prediction of Organic Reactions in Biased Data Sets via Positive-Unlabeled Learning3Enhancing Generalization in Synthesizability Prediction of Structurally Dissimilar Materials0

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  1. 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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  2. 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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  3. 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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  4. 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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  5. Bridging theory and experiment in materials discovery: Machine-learning-assisted prediction of synthesizable structures

    Physical review. B./Physical review. B · 2026-01-12

    YU Xin, P. Liu, Zhuohang Xie, Wenhui Mi, Pengyue Gao, Hong Jian Zhao, Jian Lv, Yanchao Wang, Yanming Ma

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  6. Anomaly-Detection-Driven Screening of Thermodynamic Stability from Composition Descriptors Alone

    The Journal of Physical Chemistry Letters · 2026-02-10

    Keisuke Makino, Yudai Yamaguchi, Naoto Tanibata, Hayami Takeda, RYO KOBAYASHI, Masayuki Karasuyama, Masanobu Nakayama

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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. Yield Prediction of Organic Reactions in Biased Data Sets via Positive-Unlabeled Learning

    Journal of the American Chemical Society · 2026-04-01

    Florian Boser, Jan C. Spies, Frank Glorius

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  9. Enhancing Generalization in Synthesizability Prediction of Structurally Dissimilar Materials

    Journal of Chemical Information and Modeling · 2026-07-09

    Seongmin Kim, S Kwon, Jiwoo Choi, Yousung Jung

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