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RESEARCH PAPER · Journal of Molecular Graphics and Modelling · 2019

Machine Learning Transition Temperatures from 2D Structure

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

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Machine Learning Transition Temperatures from 2D StructureOriginalMachine Learning for Harnessing Thermal Energy: From Materials Discovery to System Optimization86A Knowledge–Data Dual‐Driven Framework for Predicting the Molecular Properties of Rechargeable Battery Electrolytes18A Knowledge–Data Dual‐Driven Framework for Predicting the Molecular Properties of Rechargeable Battery Electrolytes54Introduction to Predicting Properties of Organic Materials1Machine Learning-Based Design of Anti-Electrostatic Integrated Semiconductor Bridge initiators0Predictive Modeling for Energetic Materials4Machine-learning-driven QSPR models for energetic molecules: A review on safety and energetic properties prediction13Machine learning-based design of anti-electrostatic integrated semiconductor bridge initiators0A deep neural network approach for predicting the melting point of low-molecular phase change materials2Integrated 3D Molecular Visualization and Property Prediction Using SMILES-Based Cheminformatics and Machine Learning0

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  1. Machine Learning for Harnessing Thermal Energy: From Materials Discovery to System Optimization

    ACS Energy Letters · 2022-09-02

    Man Li, Lingyun Dai, Yongjie Hu

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  2. A Knowledge–Data Dual‐Driven Framework for Predicting the Molecular Properties of Rechargeable Battery Electrolytes

    Angewandte Chemie · 2024-10-11

    Yuchen Gao, Yu‐Hang Yuan, Suozhi Huang, Nan Yao, Legeng Yu, Yao‐Peng Chen, Qiang Zhang, Xiang Chen

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  3. A Knowledge–Data Dual‐Driven Framework for Predicting the Molecular Properties of Rechargeable Battery Electrolytes

    Angewandte Chemie International Edition · 2024-10-11

    Yuchen Gao, Yu‐Hang Yuan, Suozhi Huang, Nan Yao, Legeng Yu, Yao‐Peng Chen, Qiang Zhang, Xiang Chen

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  4. Introduction to Predicting Properties of Organic Materials

    Challenges and advances in computational chemistry and physics · 2025-01-01

    Didier Mathieu

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  5. Machine Learning-Based Design of Anti-Electrostatic Integrated Semiconductor Bridge initiators

    SSRN Electronic Journal · 2025-01-01

    Wang Lei, Feng Yuzhu, Bin Zhou

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  6. Predictive Modeling for Energetic Materials

    Challenges and advances in computational chemistry and physics · 2025-01-01

    Didier Mathieu

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  7. Machine-learning-driven QSPR models for energetic molecules: A review on safety and energetic properties prediction

    Chemical Engineering Journal Advances · 2025-07-03

    Mingchi Gao, Tengxin Huang, Mingtian Li, Yingjun Zhang, Liangliang Wang, Junjie Ding

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  8. Machine learning-based design of anti-electrostatic integrated semiconductor bridge initiators

    Energetic Materials Frontiers · 2026-02-01

    Linlin Wang, Yu-zhu Feng, Bin Zhou

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  9. A deep neural network approach for predicting the melting point of low-molecular phase change materials

    npj Soft Matter · 2026-02-02

    Jingsi Liao, Wenjin Gao, Ke Xu, Wenbo Han, Yibin Li, Miao Zhou

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  10. Integrated 3D Molecular Visualization and Property Prediction Using SMILES-Based Cheminformatics and Machine Learning

    Al-Nahrain Journal for Engineering Sciences · 2026-09-20

    Marni Jacinth Raj, E. Bijolin Edwin, M. Roshni Thanka, V. Ebenezer, Stewart Kirubakaran S, Raj Thilak

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