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RESEARCH PAPER · Machine Learning Science and Technology · 2024

Impact of data bias on machine learning for crystal compound synthesizability predictions

Ali Davariashtiyani, Busheng Wang, Samad Hajinazar, Eva Zurek, Sara Kadkhodaei

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Impact of data bias on machine learning for crystal compound synthesizability predictionsOriginalInference Without Ground Truth: A Conceptual Theory of Validation in Materials AI0Algorithmic Path Dependence in AI-Driven Materials Design0Failure Case Reporting in Materials Informatics — What Is Documented and What Is Silenced0Are We Overfitting to the Materials Project? A Critique of Dataset Bias in Property Prediction Benchmarks0Utilizing Machine Learning to Advance Battery Materials Design: Challenges and Prospects51Research on Optimization Strategy Based on Fusion of Genetic Algorithm and Machine Learning Model0Accurate prediction of synthesizability and precursors of 3D crystal structures via large language models33Environmental Inequities and Global Health1Large Language Models (LLMs) for Materials Design14Integrated environment for machine learning-aided heat transfer optimisation in internal flows: hammerhead software0Artificial intelligence-driven nanomaterial design and applications: advances, challenges, and future directions0More Bang for Your Bond: Small-Molecule Kinetics as Predictors of Polymer Stability0Accelerating the Transformation of Highly Energetic Molecules from “on Paper” to Synthesizable Targets: Synthetic Accessibility Assessment with Scoring Models0Enhancing Generalization in Synthesizability Prediction of Structurally Dissimilar Materials0Machine Learning-Guided Design of ZIF-8 Polymer Nanocomposites for Sustainable Applications: Current Progress and Future Opportunities0Artificial Intelligence in Analytical Chemistry: A Critical Review of Applications, Model Performance, Validation, and Emerging Challenges0

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  1. Inference Without Ground Truth: A Conceptual Theory of Validation in Materials AI

    Journal of Artificial Intelligence for Materials Science · 2022-01-01

    Michael Turner, Daniel Brooks, Olivia Grant

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  2. Algorithmic Path Dependence in AI-Driven Materials Design

    Journal of Artificial Intelligence for Materials Science · 2023-01-01

    Maria Gonzalez, Javier Ruiz, Lucia Torres

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  3. Failure Case Reporting in Materials Informatics — What Is Documented and What Is Silenced

    Journal of Artificial Intelligence for Materials Science · 2023-01-01

    Ahmed Mansour, Omar Saeed, Lina Hassan

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  4. Are We Overfitting to the Materials Project? A Critique of Dataset Bias in Property Prediction Benchmarks

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

    Ricardo Alves, Bruno Costa, Helena Martins, Nuno Faria

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  5. Utilizing Machine Learning to Advance Battery Materials Design: Challenges and Prospects

    Chemistry of Materials · 2025-02-28

    Souvik Manna, Poulami Paul, Surya Sekhar Manna, Sandeep Das, Biswarup Pathak

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  6. Research on Optimization Strategy Based on Fusion of Genetic Algorithm and Machine Learning Model

    IEEE International Conference on Control, Electronics and Computer Technology (ICCECT) · 2025-04-25

    Zhengyu Zhou, Jiale Xu, Jiawen Gu, Chenhao Jin

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  7. Accurate prediction of synthesizability and precursors of 3D crystal structures via large language models

    Nature Communications · 2025-07-15

    Zhilong Song, Shuaihua Lu, Ming‐Gang Ju, Qionghua Zhou, Jinlan Wang

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  8. Environmental Inequities and Global Health

    IGI Global eBooks · 2025-08-12

    Kholis Ernawati

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  9. Large Language Models (LLMs) for Materials Design

    Advanced Functional Materials · 2025-12-16

    Lei Zhang, Zhaorui Liu, Ben Ni, Quan Wang

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  10. Integrated environment for machine learning-aided heat transfer optimisation in internal flows: hammerhead software

    Machine Learning Science and Technology · 2025-12-22

    Daniela Minerva Segura Galeana, Alexander Liptak, Antonio Gil, Michael G. Edwards, Aleksander Dubas, Andrew Davis

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  11. Artificial intelligence-driven nanomaterial design and applications: advances, challenges, and future directions

    New Journal of Chemistry · 2026-01-01

    Yakubu Adekunle Alli, Lukman Shehu Mustapha, Francis Mekunye, Olaoluwa John Adeleke, Terfa Jude Igba, Nelson Oshogwue Etafo, Victor D. Aderibigbe, Sreedeep Dey, Haulison Kojo Yayoh, Kehinde Shola Obayomi, Malama Chisanga

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  12. More Bang for Your Bond: Small-Molecule Kinetics as Predictors of Polymer Stability

    ACS Applied Polymer Materials · 2026-01-03

    Veerupaksh Singla, Brett Savoie

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  13. Accelerating the Transformation of Highly Energetic Molecules from “on Paper” to Synthesizable Targets: Synthetic Accessibility Assessment with Scoring Models

    The Journal of Physical Chemistry A · 2026-03-18

    Y. L. Zhang, Xiaokai He, Chao Chen, Linyuan Wen, Tao Yu, Guojin Li, Xiaoyan Wang, Zhixiang Zhang, Yingzhe Liu

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  14. 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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  15. Machine Learning-Guided Design of ZIF-8 Polymer Nanocomposites for Sustainable Applications: Current Progress and Future Opportunities

    Processes · 2026-09-09

    Huy Loc Nguyen, Thi Bich Ngoc Nguyen

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  16. Artificial Intelligence in Analytical Chemistry: A Critical Review of Applications, Model Performance, Validation, and Emerging Challenges

    Journal of the Turkish Chemical Society Section A Chemistry · 2026-10-01

    Celin Jannat Doust, Ece Özkan

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