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RESEARCH PAPER · Computational Materials Science · 2021

A machine learning approach to predict thermal expansion of complex oxides

Jian Peng, N. S. Harsha Gunda, Craig A. Bridges, Sangkeun Matthew Lee, J. Allen Haynes, Dongwon Shin

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A machine learning approach to predict thermal expansion of complex oxidesOriginalApplication of machine learning to sporadic experimental data for understanding epitaxial strain relaxation3A Neural Network Approach to Predict Gibbs Free Energy of Ternary Solid Solutions8High-Throughput Data-Driven Machine Learning Predictions of Thermal Expansion Coefficient in High-Entropy Solid Solution Carbides0Machine-learning prediction of thermal expansion coefficient for perovskite oxides with experimental validation11When Graph Convolutions Over-Smooth on Large Unit Cells: Failure Mode Analysis of Deep GNNs for Thermal Transport0Evaluating thermal expansion in fluorides and oxides: Machine learning predictions with connectivity descriptors5Review and perspective on polyhedron model for estimating thermodynamic properties of oxides7A first-principles study of ternary metal chalcogenides Ba2MnX3 (X=Te,Se,S) for efficient thermoelectric applications empowered by machine-learning interatomic potential12Explainable predictions of multi-component oxides enabled by attention-based neural networks3Boosting computational thermodynamic analysis of the CVD of SiC coating via machine learning4High-throughput data-driven machine learning prediction of thermal expansion coefficients of high-entropy solid solution carbides12Tailoring thermal expansion of β-Cu2V2O7 with improved mechanical properties by incorporation of Li+Fe3+2Machine learning assisted CALPHAD framework for thermodynamic analysis of CVD SiO N thin films2A machine learning approach to predict coefficient of friction of laser cladding high-entropy alloy coatings18High-throughput screening anisotropic negative thermal expansion framework materials2Uncertainty-aware prediction of thermal expansion in complex oxides via LLM-curated literature data0Solid oxide electrolysis technologies: Progress, challenges, and perspectives in materials engineering and machine learning approaches0

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17 direct citing papers saved.

  1. Application of machine learning to sporadic experimental data for understanding epitaxial strain relaxation

    Journal of the American Ceramic Society · 2022-09-17

    Jin Young Oh, Dongwon Shin, Woo Seok Choi

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  2. A Neural Network Approach to Predict Gibbs Free Energy of Ternary Solid Solutions

    Journal of Phase Equilibria and Diffusion · 2022-11-22

    Paul M. Laiu, Ying Yang, Massimiliano Lupo Pasini, Jong Youl Choi, Dongwon Shin

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  3. High-Throughput Data-Driven Machine Learning Predictions of Thermal Expansion Coefficient in High-Entropy Solid Solution Carbides

    SSRN Electronic Journal · 2023-01-01

    Myungjae Kim, Jiho Kim, Hyokyeong Kim, Jiwoong Kim

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  4. Machine-learning prediction of thermal expansion coefficient for perovskite oxides with experimental validation

    Physical Chemistry Chemical Physics · 2023-01-01

    Kevin P. McGuinness, Anton O. Oliynyk, Sangjoon Lee, Beatriz Molero-Sánchez, Paul Kwesi Addo

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  5. When Graph Convolutions Over-Smooth on Large Unit Cells: Failure Mode Analysis of Deep GNNs for Thermal Transport

    Journal of Computational and Data-Driven Materials Engineering · 2023-01-01

    Fatima Zahra Amrani, Youssef Benali

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  6. Evaluating thermal expansion in fluorides and oxides: Machine learning predictions with connectivity descriptors

    Chinese Physics B · 2023-04-18

    Yilin Zhang, Huimin Mu, Yuxin Cai, Xiaoyu Wang, Kun Zhou, Fuyu Tian, Yuhao Fu, Lijun Zhang

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  7. Review and perspective on polyhedron model for estimating thermodynamic properties of oxides

    Journal of the American Ceramic Society · 2023-07-22

    Elmira Moosavi‐Khoonsari, Jesus Alejandro Arias-Hernandez, Sun Yong Kwon

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  8. A first-principles study of ternary metal chalcogenides Ba2MnX3 (X=Te,Se,S) for efficient thermoelectric applications empowered by machine-learning interatomic potential

    Computational Materials Science · 2023-09-30

    Medha Rakshit, Suman Chowdhury, Arnab Majumdar, Dipali Banerjee, Debnarayan Jana

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  9. Explainable predictions of multi-component oxides enabled by attention-based neural networks

    Scripta Materialia · 2023-11-01

    Zening Yang, Weiwei Sun, Zhengyu Sun, Mutian Zhang, Jin Yu, Yubin Wen

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  10. Boosting computational thermodynamic analysis of the CVD of SiC coating via machine learning

    Journal of Crystal Growth · 2024-04-27

    Bingquan Xu, Wei Huang, Junjun Wang, Song Zhang, Zhigang Xu, Rong Tu, Wenjun Li, Jian Xi Peng, Chuanbin Wang

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  11. High-throughput data-driven machine learning prediction of thermal expansion coefficients of high-entropy solid solution carbides

    International Journal of Refractory Metals and Hard Materials · 2024-05-28

    Myungjae Kim, Jiho Kim, Jiho Kim, Hyokyeong Kim, Jiwoong Kim, Jiwoong Kim

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  12. Tailoring thermal expansion of β-Cu2V2O7 with improved mechanical properties by incorporation of Li+Fe3+

    Ceramics International · 2024-10-16

    Yumeng Xiang, Yiming Zhang, Yiming Zhang, Xiansheng Liu, Jianjun Tian, Chaoyang Kang, Feng Zhang, Feng Zhang, Jia Yu, Erjun Liang, Weifeng Zhang

    Read original paper ↗ ·

  13. Machine learning assisted CALPHAD framework for thermodynamic analysis of CVD SiO N thin films

    Calphad · 2025-02-09

    Junjun Wang, Bingquan Xu, Bingquan Xu, Kyungjun Lee, Wei Huang, Huihui Wang, Jian Peng, Man Xu

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  14. A machine learning approach to predict coefficient of friction of laser cladding high-entropy alloy coatings

    Journal of Materials Research and Technology · 2025-05-01

    Yawen Pan, Bin Xu, Shupeng Xu, Long Liu, Junjun Wang, Jian Peng, Zhigang Xu, Wenjun Li, Qiang Shen, Chuanbin Wang

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  15. High-throughput screening anisotropic negative thermal expansion framework materials

    Acta Materialia · 2025-09-04

    Zhikai Gao, Xianteng Zhou, Meng Xu, Yuanji Xu, Fuyang Tian

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  16. Uncertainty-aware prediction of thermal expansion in complex oxides via LLM-curated literature data

    APL Machine Learning · 2026-07-20

    Jordan Marshall, Marlena Alexander, Vincent Joseph García, Joshua Safin, Sheryl L. Sanchez, Elham Foadian, Claudia J. Rawn, Katharine Lynn Page, Sergei V. Kalinin, Mahshid Ahmadi

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  17. Solid oxide electrolysis technologies: Progress, challenges, and perspectives in materials engineering and machine learning approaches

    Renewable and Sustainable Energy Reviews · 2026-10-07

    Younes Ait Bella, Macmillan Chanda, Houda Karibe, Samir Salhy, Said Sair, Abdessamad Faik, Hanane Ait Ousaleh

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