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RESEARCH PAPER · Journal of Chemical Information and Modeling · 2023

Identification of High-Reliability Regions of Machine Learning Predictions Based on Materials Chemistry

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

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Identification of High-Reliability Regions of Machine Learning Predictions Based on Materials ChemistryOriginalRapid Discovery of Gas Response in Materials Via Density Functional Theory and Machine Learning24Machine learning materials properties with accurate predictions, uncertainty estimates, domain guidance, and persistent online accessibility17Local Adaptive Fusion Regression (LAFR) for Local Linear Multivariate Calibration: Application to Large Datasets9A general approach for determining applicability domain of machine learning models56Machine learning and its impact on advanced membrane materials0Simultaneous enhancement of the room temperature and high-temperature properties of Al-Cu-Mg-Ag alloys via machine learning on small datasets2Pharmacologic reversal of advanced Alzheimer’s disease in mice and identification of potential therapeutic nodes in human brain14Interpretable machine learning analysis of stability mechanisms and feature interactions in halide perovskite solar cells0D2VP: A Visual First Platform for Diagnosing Data Distribution Mismatch Toward Reliable Machine Learning in Materials Science2

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  1. Rapid Discovery of Gas Response in Materials Via Density Functional Theory and Machine Learning

    Energy & environment materials · 2024-08-02

    Shasha Gao, Yongchao Cheng, Chen Lü, Sheng Guo Huang

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  2. Machine learning materials properties with accurate predictions, uncertainty estimates, domain guidance, and persistent online accessibility

    Machine Learning Science and Technology · 2024-12-01

    Ryan M. Jacobs, Lane E. Schultz, Aristana Scourtas, K. J. Schmidt, Owen Price-Skelly, Will Engler, Ian T Foster, Ben J. Blaiszik, Paul M. Voyles, Dane D. Morgan

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  3. Local Adaptive Fusion Regression (LAFR) for Local Linear Multivariate Calibration: Application to Large Datasets

    Applied Spectroscopy · 2025-01-23

    Robert C. Spiers, John H. Kalivas

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  4. A general approach for determining applicability domain of machine learning models

    npj Computational Materials · 2025-04-05

    Lane E. Schultz, Yiqi Wang, Ryan M. Jacobs, Dane D. Morgan

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  5. Machine learning and its impact on advanced membrane materials

    Elsevier eBooks · 2025-09-27

    Kiran Mustafa, Mashallah Rezakazemi, Rao Muhammad Mahtab Mahboob

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  6. Simultaneous enhancement of the room temperature and high-temperature properties of Al-Cu-Mg-Ag alloys via machine learning on small datasets

    Materials Characterization · 2025-12-10

    Y.Q. Wang, Hao Dong, Yongchun Guo, Yanru Shao, H. Li, Y. Liang, Quangui Guo, F. Xia, Zhiyu Yang, J.P. Li

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  7. Pharmacologic reversal of advanced Alzheimer’s disease in mice and identification of potential therapeutic nodes in human brain

    Cell Reports Medicine · 2025-12-22

    Kalyani Chaubey, Edwin Vázquez‐Rosa, Sunil Jamuna Tripathi, Min‐Kyoo Shin, Youngmin Yu, Matasha Dhar, Suwarna Chakraborty, Mai Yamakawa, Xinming Wang, Preethy S. Sridharan, Emiko Miller, Zea Bud, Sofia G. Corella, Sarah Barker, Salvatore G. Caradonna, Yeojung Koh, Kathryn Franke, Coral J. Cintrón-Pérez, Sophia Rose, Hua Fang, Adrian A. Cintrón-Pérez, Taylor Tomco, Xiongwei Zhu, Hisashi Fujioka, Tamar D. Gefen, Margaret E Flanagan, Noelle Sevilir Williams, Brigid M. Wilson, Lawrence Chen, Lijun Dou, Feixiong Cheng, Jessica E. Rexach, Jung-A Woo, David E. Kang, Bindu Diana Paul, Andrew A. Pieper

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  8. Interpretable machine learning analysis of stability mechanisms and feature interactions in halide perovskite solar cells

    Journal of Materials Science · 2026-04-10

    Mohd Taukeer Khan, Saleh O. Allehabi

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  9. D2VP: A Visual First Platform for Diagnosing Data Distribution Mismatch Toward Reliable Machine Learning in Materials Science

    ChemPhysChem · 2026-05-14

    Xuqiang Shao, Zhaoyan Dong, Piao Ma, Limin Li, Tongao Yao, Yujie Yan, Mingzhe Li, Zhengyang Gao, Weijie Yang

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