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RESEARCH PAPER · Computational Materials Science · 2019

ThermoEPred-EL: Robust bandgap predictions of chalcogenides with diamond-like structure via feature cross-based stacked ensemble learning

Xiangmeng Wang, Yonglin Xu, Jiong Yang, Jianyue Ni, Wu Zhang, Wenhao Zhu

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42 direct citing works

ThermoEPred-EL: Robust bandgap predictions of chalcogenides with diamond-like structure via feature cross-based stacked ensemble learningOriginalA steel property optimization model based on the XGBoost algorithm and improved PSO200Performance evaluation of ensemble learning techniques for landslide susceptibility mapping at the Jinping county, Southwest China68Machine learning band gaps from the electron density18Accurate and explainable machine learning for the power factors of diamond-like thermoelectric materials17Recent progress in the machine learning-assisted rational design of alloys69Green Chemistry Applied to Transition Metal Chalcogenides through Synthesis, Design of Experiments, Life Cycle Assessment, and Machine Learning5High-Throughput Prediction of the Band Gaps of van der Waals Heterostructures via Machine Learning11Feature selection method reducing correlations among features by embedding domain knowledge101Bandgap prediction on small thermoelectric material dataset via instance-based transfer learning10An artificial intelligence model based on multi-step feature engineering and deep attention network for optical network performance monitoring17Robust SSRL analysis framework for intervention strategy construction in CSCL environment5Data quantity governance for machine learning in materials science228New Materials and Devices for Thermoelectric Power Generation6Using Machine Learning Techniques to Discover Novel Thermoelectric Materials4Accurate and rapid predictions with explainable graph neural networks for small high-fidelity bandgap datasets2Recent progress on machine learning with limited materials data: Using tools from data science and domain knowledge31Developing new electrocatalysts for oxygen evolution reaction via high throughput experiments and artificial intelligence35A Review on the Application of Superalloys Composition, Microstructure, Processing, and Performance via Machine Learning18The usability of stacking-based ensemble learning model in crime prediction: a systematic review3EEG Signal-Based Sentiment Analysis for Brain-Computer Interfaces: A Comprehensive Comparison Between Stacking-Ensemble and Deep Learning Approaches0Descriptors Divide‐and‐Conquer Enables Multifaceted and Interpretable Materials Structure–Activity Relationship Analysis24Machine Learning Insights into Band Alignments of van der Waals Heterostructures2A review of machine learning approaches for the discovery of thermoelectric materials3Data-driven prediction of structural and mechanical properties of rare-earth pyrochlores0A machine learning-driven digital twin framework for mold optimization and safety assessment in highly filled viscous materials extrusion0

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  1. A steel property optimization model based on the XGBoost algorithm and improved PSO

    Computational Materials Science · 2019-12-17

    Kai Song, Feng Yan, Ting Ding, Liang Gao, Songbao Lu

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  2. Performance evaluation of ensemble learning techniques for landslide susceptibility mapping at the Jinping county, Southwest China

    Natural Hazards · 2020-10-17

    Xudong Hu, Hongbo Mei, Han Zhang, Yuanyuan Li, Mengdi Li

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  3. Machine learning band gaps from the electron density

    Physical Review Materials · 2021-08-10

    Javier Robledo Moreno, Johannes Flick, Antoine Georges

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  4. Accurate and explainable machine learning for the power factors of diamond-like thermoelectric materials

    Journal of Materiomics · 2021-11-23

    Zhe Yang, Ye Sheng, Cong Zhu, Jianyue Ni, Zhenyu Zhu, Jinyang Xi, Wu Zhang, Jiong Yang

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  5. Recent progress in the machine learning-assisted rational design of alloys

    International Journal of Minerals Metallurgy and Materials · 2022-04-01

    Huadong Fu, Hong‐Tao Zhang, Changsheng Wang, Wei Yong, Jianxin Xie

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  6. Green Chemistry Applied to Transition Metal Chalcogenides through Synthesis, Design of Experiments, Life Cycle Assessment, and Machine Learning

    IntechOpen eBooks · 2022-05-20

    Alexandre H. Pinto, Dylan R. Cho, Anton O. Oliynyk, Julian R. Silverman

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  7. High-Throughput Prediction of the Band Gaps of van der Waals Heterostructures via Machine Learning

    Nanomaterials · 2022-07-04

    Rui Hu, Wen Lei, Hongmei Yuan, Shihao Han, Huijun Liu

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  8. Feature selection method reducing correlations among features by embedding domain knowledge

    Acta Materialia · 2022-07-22

    Yue Liu, Xinxin Zou, Shuchang Ma, Maxim Avdeev, Siqi Shi

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  9. Bandgap prediction on small thermoelectric material dataset via instance-based transfer learning

    Computational and Theoretical Chemistry · 2022-09-07

    Cong Zhu, Jianyue Ni, Zhe Yang, Ye Sheng, Jiong Yang, Wu Zhang

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  10. An artificial intelligence model based on multi-step feature engineering and deep attention network for optical network performance monitoring

    Optik · 2022-12-19

    Yang Zhou, Zhuojia Yang, Qiang Sun, Chengqing Yu, Chengming Yu, Chengming Yu, Chengming Yu

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  11. Robust SSRL analysis framework for intervention strategy construction in CSCL environment

    Heliyon · 2023-03-01

    Chengzheng Li, Peng Peng, Cao Lei

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  12. Data quantity governance for machine learning in materials science

    National Science Review · 2023-05-01

    Yue Liu, Zhengwei Yang, Xinxin Zou, Shuchang Ma, Dahui Liu, Maxim Avdeev, Siqi Shi

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  13. New Materials and Devices for Thermoelectric Power Generation

    IntechOpen eBooks · 2023-10-25

    Basel I. Abed Ismail

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  14. Using Machine Learning Techniques to Discover Novel Thermoelectric Materials

    IntechOpen eBooks · 2023-10-31

    Ebrar Yıldırım, Övgü Ceyda Yelgel

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  15. Accurate and rapid predictions with explainable graph neural networks for small high-fidelity bandgap datasets

    Modelling and Simulation in Materials Science and Engineering · 2024-01-25

    Jianping Xiao, Li Yang, Shuqun Wang

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  16. Recent progress on machine learning with limited materials data: Using tools from data science and domain knowledge

    Journal of Materiomics · 2024-07-26

    Bangtan Zong, Jinshan Li, Tinghuan Yuan, Jun Wang, Ruihao Yuan

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  17. Developing new electrocatalysts for oxygen evolution reaction via high throughput experiments and artificial intelligence

    npj Computational Materials · 2024-08-28

    S. L. XU, Zhuyang Chen, Mingyang Qin, Bijun Cai, Weixuan Li, Ronggui Zhu, Chen Guang Xu, X.‐D. Xiang

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  18. A Review on the Application of Superalloys Composition, Microstructure, Processing, and Performance via Machine Learning

    JOM · 2024-10-10

    Junhui Zhang, Haiyan Gao, Yahui Liu, Jun Wang

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  19. The usability of stacking-based ensemble learning model in crime prediction: a systematic review

    Crime Prevention and Community Safety · 2024-11-19

    Cem EROĞLU, Hüseyin Çakır

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  20. EEG Signal-Based Sentiment Analysis for Brain-Computer Interfaces: A Comprehensive Comparison Between Stacking-Ensemble and Deep Learning Approaches

    IEEE India Council International Conference (INDICON) · 2024-12-19

    Sapthak Mohajon Turjya, Raj Singh, Bibhuti Bhusan Dash, Mahendra Kumar Gourisaria, Sudhansu Shekhar Patra, Anjan Bandyopadhyay

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  21. Descriptors Divide‐and‐Conquer Enables Multifaceted and Interpretable Materials Structure–Activity Relationship Analysis

    Advanced Functional Materials · 2025-02-11

    Yue Liu, Linhan Wu, Zhengwei Yang, Xinxin Zou, Zheyi Zou, Yuxiao Lin, Maxim Avdeev, Siqi Shi

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  22. Machine Learning Insights into Band Alignments of van der Waals Heterostructures

    The Journal of Physical Chemistry C · 2025-06-09

    Ruo-Fan Shen, Ya-Chao Liu, Wanli Jia, Jia Xin Shi, Yoshiyuki Kawazoe, Vei Wang

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  23. A review of machine learning approaches for the discovery of thermoelectric materials

    Advances in Physics X · 2025-07-27

    Övgü Ceyda Yelgel, Celal Yelgel

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  24. Data-driven prediction of structural and mechanical properties of rare-earth pyrochlores

    Journal of Alloys and Compounds Communications · 2025-10-09

    Mohamed Boubchir, Rachid Boubchir, Nadir Mohamed Belmessaoud, H. Aourag

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  25. A machine learning-driven digital twin framework for mold optimization and safety assessment in highly filled viscous materials extrusion

    Journal of Manufacturing Processes · 2026-08-11

    Zihan Zhou, Xin Zhuang, Chenglong Xu, Yu Wang, Guo Li, Jiangning Wang, Linsheng Xie

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