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RESEARCH PAPER · Applied Physics Reviews · 2023

ET-AL: Entropy-targeted active learning for bias mitigation in materials data

James M. Rondinelli, Wei Chen

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ET-AL: Entropy-targeted active learning for bias mitigation in materials dataOriginalAlgorithmic Path Dependence in AI-Driven Materials Design0Diverse 3D auxetic unit cell inverse design with deep learning21In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science99Machine learning-based evaluation of functional characteristics of Li-rich layered oxide cathode materials using the data of XPS and XRD spectra16Exploiting redundancy in large materials datasets for efficient machine learning with less data118Multi-principal element alloy discovery using directed energy deposition and machine learning35Data‐Driven Design for Metamaterials and Multiscale Systems: A Review235Not as simple as we thought: a rigorous examination of data aggregation in materials informatics13Oxide ceramics of A2M3O12 family with negative and close-to-zero thermal expansion coefficients: Machine learning-based modeling of functional characteristics12Investigating the Dielectric Behavior of Zethrene-Inspired Nanoscale System: Monte Carlo Approach0Roadmap on data-centric materials science27Stability Analysis of Deep Neural Networks under Adversarial Attacks and Noise Perturbations1Designing unique and high-performance Al alloys via machine learning: Mitigating data bias through active learning24On the Data Quality and Imbalance in Machine Learning-based Design and Manufacturing—A Systematic Review106Bearing fault diagnosis based on novel hierarchical multiscale dispersion entropy in corresponding color block images2Impact of data bias on machine learning for crystal compound synthesizability predictions16Coherent collections of rules describing exceptional materials identified with a multi-objective optimization of subgroups2Scientific Risk in Autonomous Materials Discovery: A Conceptual Framework for Anticipating Unsafe Recommendations1Active learning of molecular data for task-specific objectives4Probing out-of-distribution generalization in machine learning for materials81Machine Learning-enhanced Copper (I) Thiocyanate-based Perovskite-silicon Tandem Solar Cells: Optimization Strategies for Enhanced Effi ciency and Stability1Emerging Microelectronic Materials by Design: Navigating Combinatorial Design Space with Scarce and Dispersed Data6Recent Progress of Artificial Intelligence Application in Polymer Materials54Impact of Domain Knowledge on the Property Prediction of Specialized Machine Learning Models5Human Oversight as System Design: A Conceptual Reframing of Control in Semi-Autonomous Materials AI0Tailoring Materials Design for Aqueous Energy Storage and Conversion through Electrochemical Reconstruction20D2VP: A Visual First Platform for Diagnosing Data Distribution Mismatch Toward Reliable Machine Learning in Materials Science2Detecting concept drift in imbalanced data streams with limited labels0Active learning with heteroscedastic Gaussian Process Regression model1Where Do Crystal Graph Networks Fail? A Space-Group-Resolved Error Analysis of Band Gap Prediction with a Simple Edge-Aware GNN: Crystal-X0

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  2. Diverse 3D auxetic unit cell inverse design with deep learning

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  3. In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science

    Journal of the American Chemical Society · 2023-09-27

    Joshua Schrier, Alexander J. Norquist, Tonio Buonassisi, Jakoah Brgoch

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  4. Machine learning-based evaluation of functional characteristics of Li-rich layered oxide cathode materials using the data of XPS and XRD spectra

    Computational Materials Science · 2023-10-30

    Natalia V. Kireeva, Vladislav S. Pervov, Aslan Yu. Tsivadze

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  5. Exploiting redundancy in large materials datasets for efficient machine learning with less data

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    Kangming Li, Daniel Persaud, Kamal Kumar Choudhary, Brian DeCost, Michael Greenwood, Jason R. Hattrick-Simpers

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  6. Multi-principal element alloy discovery using directed energy deposition and machine learning

    Materials Science and Engineering A · 2023-11-28

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  7. Data‐Driven Design for Metamaterials and Multiscale Systems: A Review

    Advanced Materials · 2023-12-05

    Doksoo Lee, Wei Chen, Wei (Wayne) Chen, Liwei Wang, Wei Chen, Wei Chen, Wei Chen

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  8. Not as simple as we thought: a rigorous examination of data aggregation in materials informatics

    Digital Discovery · 2023-12-28

    Federico Ottomano, Giovanni de Felice, Vladimir V. Gusev, Taylor D. Sparks

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  9. Oxide ceramics of A2M3O12 family with negative and close-to-zero thermal expansion coefficients: Machine learning-based modeling of functional characteristics

    Journal of Alloys and Compounds · 2024-04-08

    Natalia V. Kireeva, Aslan Yu. Tsivadze

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  10. Investigating the Dielectric Behavior of Zethrene-Inspired Nanoscale System: Monte Carlo Approach

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    H. Eraki, N. Saber, Zakaria Fadil, A. Mhirech, B. Kabouchi, L. Bahmad, Steve Karam, Chaitany Jayprakash Raorane, Abdulrahman A. Alsayyari, Seong‐Cheol Kim

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  11. Roadmap on data-centric materials science

    Modelling and Simulation in Materials Science and Engineering · 2024-05-17

    Stefan Bauer, Peter Benner, Tristan Bereau, Volker Blüm, Mario Boley, Christian Carbogno, C. Richard A. Catlow, Gerhard Dehm, Sebastian Eibl, Ralph Ernstorfer, Ádám Fekete, Lucas Foppa, Peter Fratzl, Christoph Freysoldt, Baptiste Gault, Luca M. Ghiringhelli, Sajal Kumar Giri, Anton Gladyshev, Pawan Goyal, Jason R. Hattrick-Simpers, Lara Kabalan, Petr Karpov, Mohammad Sarkari Khorrami, Christoph T. Koch, Sebastian Kokott, Thomas Kosch, Igor Kowalec, Kurt Kremer, Andreas Leitherer, Yue Li, Christian H. Liebscher, Andrew James Logsdail, Zhongwei Lu, Phuc Luong, Andreas Marek A, Florian Merz, Jaber Rezaei Mianroodi, Jörg Neugebauer, Zongrui Pei, Thomas A. R. Purcell, Dierk Raabe, Markus Rampp, Mariana Rossi, Jan Michael Rost, James E. Saal, Ulf Saalmann, Kasturi Narasimha Sasidhar, Alaukik Saxena, Luigi Sbailò, Markus Scheidgen, Marcel Schloz, Daniel Francis Schmidt, Simon Teshuva, Annette Trunschke, Ye Wei, Gerhard Weikum, Rui Patrick Xian, Yiyu Y. Yao, Junqi Yin, Meng Zhao, Matthias Scheffler

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  12. Stability Analysis of Deep Neural Networks under Adversarial Attacks and Noise Perturbations

    International Wireless Communications and Mobile Computing Conference (IWCMC) · 2024-05-27

    Parisa Eslami, Houbing Herbert Song

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  13. Designing unique and high-performance Al alloys via machine learning: Mitigating data bias through active learning

    Computational Materials Science · 2024-07-01

    Mingwei Hu, Qiyang Tan, Ruth Knibbe, Miao Xu, Guofang Liang, Jianxin Zhou, Jun Xu, Bin Jiang, Xue Li, Mahendra Ramajayam, Thomas Dorin, Mingxing Zhang

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  14. On the Data Quality and Imbalance in Machine Learning-based Design and Manufacturing—A Systematic Review

    Engineering · 2024-07-02

    Jiarui Xie, Lijun Sun, Yaoyao Fiona Zhao

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  15. Bearing fault diagnosis based on novel hierarchical multiscale dispersion entropy in corresponding color block images

    Engineering Research Express · 2024-10-16

    Zihan Wang, Jigang Peng, Yongjian Sun

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  16. Impact of data bias on machine learning for crystal compound synthesizability predictions

    Machine Learning Science and Technology · 2024-11-15

    Ali Davariashtiyani, Busheng Wang, Samad Hajinazar, Eva Zurek, Sara Kadkhodaei

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  17. Coherent collections of rules describing exceptional materials identified with a multi-objective optimization of subgroups

    Digital Discovery · 2025-01-01

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  18. Scientific Risk in Autonomous Materials Discovery: A Conceptual Framework for Anticipating Unsafe Recommendations

    Journal of Artificial Intelligence for Materials Science · 2025-01-01

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  19. Active learning of molecular data for task-specific objectives

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  20. Probing out-of-distribution generalization in machine learning for materials

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  21. Machine Learning-enhanced Copper (I) Thiocyanate-based Perovskite-silicon Tandem Solar Cells: Optimization Strategies for Enhanced Effi ciency and Stability

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  22. Emerging Microelectronic Materials by Design: Navigating Combinatorial Design Space with Scarce and Dispersed Data

    Accounts of Materials Research · 2025-05-05

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  23. Recent Progress of Artificial Intelligence Application in Polymer Materials

    Polymers · 2025-06-16

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  24. Impact of Domain Knowledge on the Property Prediction of Specialized Machine Learning Models

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  25. Human Oversight as System Design: A Conceptual Reframing of Control in Semi-Autonomous Materials AI

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  26. Tailoring Materials Design for Aqueous Energy Storage and Conversion through Electrochemical Reconstruction

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

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  30. Where Do Crystal Graph Networks Fail? A Space-Group-Resolved Error Analysis of Band Gap Prediction with a Simple Edge-Aware GNN: Crystal-X

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