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RESEARCH PAPER · Communications Materials · 2025

Advancing extrapolative predictions of material properties through learning to learn using extrapolative episodic training

Kohei Noda, Araki Wakiuchi, Yoshihiro Hayashi, Ryo Yoshida

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13 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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42 direct citing works

Advancing extrapolative predictions of material properties through learning to learn using extrapolative episodic trainingOriginalOut-of-Distribution Generalization in Materials AI: A Systematic Review of Domain Shift, Robustness, and What Remains Unsolved0Accelerating age hardening behavior of Mg alloy using a data-driven screening strategy guided by machine learning3AI‐Assisted Design of Advanced Polymeric Materials: Challenges and Solutions25Known Unknowns: Out-of-Distribution Property Prediction in Materials and Molecules29Extrapolative prediction of polymer properties using physics-informed hierarchical descriptors4Cognitive AI beyond prediction: toward reasoning and discovery1AI-powered open-source infrastructure for accelerating materials discovery and advanced manufacturing27Machine learning in thermal spraying: applications, challenges, and future perspectives2Machine Learning and Domain Adaptation for Material Generalization in Electrocatalyst Evaluation: A Comprehensive Review2Small-data machine learning for resolving degradation challenges in energy devices2A two-stage machine-learning framework for the discovery of stable boron-based and sulfur-terminated MXenes as promising hydrogen evolution reaction catalysts0Extrapolative machine learning for resource-efficient dyeing recipe design in recycled PET/PCT microfiber0Polymer informatics for decision-making in industrial R&D: Challenges, opportunities, and perspectives0

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13 direct citing papers saved.

  1. Out-of-Distribution Generalization in Materials AI: A Systematic Review of Domain Shift, Robustness, and What Remains Unsolved

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

    Nikolai Ivanov, Sergey Volkov, Elena Morozova

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  2. Accelerating age hardening behavior of Mg alloy using a data-driven screening strategy guided by machine learning

    Next Materials · 2025-11-17

    Atwakyire Moses, Qiao Yun Huang, Ding Chen

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  3. AI‐Assisted Design of Advanced Polymeric Materials: Challenges and Solutions

    Advanced Materials · 2025-11-20

    Liang Gao, Siqin Song, Jiaping Lin, Yisheng Xu, Liquan Wang, Lei Du

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  4. Known Unknowns: Out-of-Distribution Property Prediction in Materials and Molecules

    npj Computational Materials · 2025-11-20

    Nofit Segal, Aviv Netanyahu, Kevin P. Greenman, Pulkit Agrawal, Rafael Gómez‐Bombarelli

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  5. Extrapolative prediction of polymer properties using physics-informed hierarchical descriptors

    Applied Physics Letters · 2025-12-08

    Hiroto Yokoyama, Takahiro Umemoto, Akiko Kumada, Masahiro Sato

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  6. Cognitive AI beyond prediction: toward reasoning and discovery

    Materials Horizons · 2026-01-01

    Jianliang Gong, Han Zhou, Shicheng Yu, Yiwang Chen

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  7. 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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  8. Machine learning in thermal spraying: applications, challenges, and future perspectives

    Surface Science and Technology · 2026-03-26

    Han Gao, Xudong Nie, Jinyong Xu, Sihao Deng, Hanlin Liao, Chao Zhang

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  9. Machine Learning and Domain Adaptation for Material Generalization in Electrocatalyst Evaluation: A Comprehensive Review

    Archives of Computational Methods in Engineering · 2026-04-17

    Parmeshwar U. Laghane, Bhaskar R. Sathe, Pravin Laxmikant Yannawar

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  10. Small-data machine learning for resolving degradation challenges in energy devices

    APL Machine Learning · 2026-05-05

    Shuan Cheng, Xiao Cui, Hemanth Neelgund Ramesh, William C. Chueh, Shijing Sun

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  11. A two-stage machine-learning framework for the discovery of stable boron-based and sulfur-terminated MXenes as promising hydrogen evolution reaction catalysts

    International Journal of Hydrogen Energy · 2026-05-20

    Hien Doan-Thi, Linh Vu-Thanh-Thuy, Pham Do Chung, Linh Tran-Phan-Thuy, Do Danh Bich, Hoang Luc-Huy, Pham Van Hai

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  12. Extrapolative machine learning for resource-efficient dyeing recipe design in recycled PET/PCT microfiber

    Digital Chemical Engineering · 2026-08-15

    H.S. Cho, Seung Geol Lee

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  13. Polymer informatics for decision-making in industrial R&D: Challenges, opportunities, and perspectives

    MRS Bulletin · 2026-10-08

    拓也 南, Katsuhisa Yoshida

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