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RESEARCH PAPER · The Journal of Physical Chemistry Letters · 2024

Physics-Guided Dual Self-Supervised Learning for Structure-Based Material Property Prediction

Nihang Fu, Lai Wei, Jianjun Hu

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

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Physics-Guided Dual Self-Supervised Learning for Structure-Based Material Property PredictionOriginalDevelopment and application of Few-shot learning methods in materials science under data scarcity47Self-supervised probabilistic models for exploring shape memory alloys10Enhancing material property prediction with ensemble deep graph convolutional networks21Fast Eis Reconstruction Strategy Based on Sim2real Theory and Physics Informed Self-Supervised Learning Approach0Multi-level Δ-learning for predicting the radiative decay rate constant of phosphorescent platinum( ii ) complexes3A Perspective on Foundation Models in Chemistry58What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials2Application of self-supervised learning in steel surface defect detection9Supervised pretraining for material property prediction5SSFormer: Self-supervised transformer model for predicting the properties of crystalline materials1In context learning foundation models for materials property prediction with small datasets1Attention-based composition learning for thermodynamic and electronic property prediction in inorganic materials0AI-driven property prediction and design optimisation of composite materials: A review0

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  1. Development and application of Few-shot learning methods in materials science under data scarcity

    Journal of Materials Chemistry A · 2024-01-01

    Yongxing Chen, Long Peng, Bin Liu, Yi Wang, Junlong Wang, Tian Ma, Huilin Wei, Yue Kang, Haining Ji

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  2. Self-supervised probabilistic models for exploring shape memory alloys

    npj Computational Materials · 2024-08-15

    Yiding Wang, Tianqing Li, Hongxiang Zong, Xiangdong Ding, Songhua Xu, Jun Sun, Turab Lookman

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  3. Enhancing material property prediction with ensemble deep graph convolutional networks

    Frontiers in Materials · 2024-10-10

    C. M. A. Rahman, Ghadendra Bhandari, Nasser M. Nasrabadi, Aldo Humberto Romero, Prashnna Gyawali

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  4. Fast Eis Reconstruction Strategy Based on Sim2real Theory and Physics Informed Self-Supervised Learning Approach

    SSRN Electronic Journal · 2025-01-01

    BOHUI LIANG, Xuemei Wang, Longyun Kang, DI XIE, Liangliang Wang, SHOUMO WANG, MENGCHUAN ZHANG

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  5. Multi-level Δ-learning for predicting the radiative decay rate constant of phosphorescent platinum( ii ) complexes

    Journal of Materials Chemistry C · 2025-01-01

    Shuai Wang, ChiYung Yam, Lihong Hu, Faan‐Fung Hung, Shuguang Chen, Chi‐Ming Che, GuanHua Chen

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  6. A Perspective on Foundation Models in Chemistry

    JACS Au · 2025-03-25

    Junyoung Choi, Gunwook Nam, Jaesik Choi, Yousung Jung

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  7. What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

    Advanced Intelligent Discovery · 2025-06-20

    Jonathan Y. C. Ting, Amanda Susan Barnard

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  8. Application of self-supervised learning in steel surface defect detection

    Journal of Materials Informatics · 2025-07-22

    Shiyu Hu, Xudong Ma, Yuqi Zhang, Wei Xu

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  9. Supervised pretraining for material property prediction

    Communications Materials · 2025-11-26

    C. M. A. Rahman, Aldo Humberto Romero, Prashnna Gyawali

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  10. SSFormer: Self-supervised transformer model for predicting the properties of crystalline materials

    Computational Materials Science · 2025-12-22

    Jie Feng, Tao Sun, Jianmei Yuan

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  11. In context learning foundation models for materials property prediction with small datasets

    npj Computational Materials · 2026-04-24

    Qinyang Li, Rongzhi Dong, Nicholas Miklaucic, Jeffrey Hu, Jeffrey Hu, Sadman Sadeed Omee, Lai Wei, Sourin Dey, Ming Hu, Jianjun Hu, Jianjun Hu

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  12. Attention-based composition learning for thermodynamic and electronic property prediction in inorganic materials

    Next Materials · 2026-08-25

    Purnachary M, Avula Edukondalu, T. Adilakshmi

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  13. AI-driven property prediction and design optimisation of composite materials: A review

    Journal of Reinforced Plastics and Composites · 2026-09-15

    Sudhanshu Singh, Ranjna Kumari, Zunjarrao Kamble, Ghanshyam Neje

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