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RESEARCH PAPER · Materials Science and Engineering B · 2019

SEHC: A high-throughput materials computing framework with automatic self-evaluation filtering

Wenhao Zhu, Yonglin Xu, Jianyue Ni, Guannan Hu, Xiangmeng Wang, Wu Zhang

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12 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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SEHC: A high-throughput materials computing framework with automatic self-evaluation filteringOriginalHigh‐throughput computational materials screening and discovery of optoelectronic semiconductors100Accelerating materials discovery using machine learning238JAMIP: an artificial-intelligence aided data-driven infrastructure for computational materials informatics85Machine-learning-assisted molecular design of phenylnaphthylamine-type antioxidants11Predicting the particle-agglomeration effect on the equivalent mechanical properties of dispersion nuclear fuel by machine learning16Advances in data‐assisted high‐throughput computations for material design112Scope of machine learning in materials research—A review230Novel direct and wide bandgap SiC semiconductors: High-throughput screening and density functional theory5Accelerating material discovery with a threshold-driven hybrid acquisition policy-based Bayesian optimization8Artificial Intelligence for Materials Discovery, Development, and Optimization171Compositional design of Ni-based superalloys for additive manufacturing: Progress and perspectives9A review on solid–liquid triboelectric nanogenerators for energy harvesting with machine learning and deep learning-based design optimization1

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  1. High‐throughput computational materials screening and discovery of optoelectronic semiconductors

    Wiley Interdisciplinary Reviews Computational Molecular Science · 2020-06-29

    Shulin Luo, Tianshu Li, Xinjiang Wang, Muhammad Faizan, Lijun Zhang

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  2. Accelerating materials discovery using machine learning

    Journal of Material Science and Technology · 2020-12-24

    Yongfei Juan, Yongbing Dai, Yang Yang, Jiao Zhang

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  3. JAMIP: an artificial-intelligence aided data-driven infrastructure for computational materials informatics

    中国科学通报：英文版 · 2021-06-15

    Xingang Zhao, Kun Zhou, Bangyu Xing, Ruoting Zhao, Shulin Luo, Tianshu Li, Yuanhui Sun, Guangren Na, Jiahao Xie, Xiaoyu Yang, Xinjiang Wang, Xiaoyu Wang, Xin He, Jian Lv, Yuhao Fu, Lijun Zhang

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  4. Machine-learning-assisted molecular design of phenylnaphthylamine-type antioxidants

    Physical Chemistry Chemical Physics · 2022-01-01

    Shanda Du, Xiujuan Wang, Runguo Wang, Ling Lu, Yanlong Luo, Guohua You, Sizhu Wu

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  5. Predicting the particle-agglomeration effect on the equivalent mechanical properties of dispersion nuclear fuel by machine learning

    Journal of Nuclear Materials · 2023-08-27

    Yingxuan Dong, Junnan Lv, Tao Peng, Hong Zuo, Qun Li

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  6. Advances in data‐assisted high‐throughput computations for material design

    Materials Genome Engineering Advances · 2023-09-01

    Dingguo Xu, Qiao Zhang, Xiangyu Huo, Yitong Wang, Mingli Yang

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  7. Scope of machine learning in materials research—A review

    Applied Surface Science Advances · 2023-11-28

    Md Hosne Mobarak, Mariam Akter Mimona, Md. Aminul Islam, Nayem Hossain, Fatema Tuz Zohura, Ibnul Imtiaz, Md Israfil Hossain Rimon

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  8. Novel direct and wide bandgap SiC semiconductors: High-throughput screening and density functional theory

    Results in Physics · 2024-09-13

    Heng Liu, Mengjiang Xing, Qingyang Fan

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  9. Accelerating material discovery with a threshold-driven hybrid acquisition policy-based Bayesian optimization

    Manufacturing Letters · 2024-10-01

    Ahmed Shoyeb Raihan, Hamed Khosravi, Srinjoy Das, Imtiaz Ahmed

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  10. Artificial Intelligence for Materials Discovery, Development, and Optimization

    ACS Nano · 2025-07-25

    Benediktus Madika, Aditi Saha, Chaeyul Kang, Batzorig Buyantogtokh, Joshua Agar, Chris M. Wolverton, Peter W. Voorhees, P. B. Littlewood, Sergei V. Kalinin, Seungbum Hong

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  11. Compositional design of Ni-based superalloys for additive manufacturing: Progress and perspectives

    Materials Science and Engineering R Reports · 2026-01-14

    Bingbing Yin, Yong Wei, Jiarui Zhu, Haoyu Ge, Shuaicheng Zhu, Yunwei Gui, Wenjing Zhang, Huadong Fu, Jinghe Xie

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  12. A review on solid–liquid triboelectric nanogenerators for energy harvesting with machine learning and deep learning-based design optimization

    Next Energy · 2026-06-29

    B.M. Siddharth, Srujan Sapkal, Himanshu Sekhar Panda

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