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RESEARCH PAPER · Applied Physics Reviews · 2024

Designing workflows for materials characterization

Sergei V. Kalinin, Maxim A Ziatdinov, Mahshid Ahmadi, Ayana Ghosh, Kevin M. Roccapriore, Yongtao Liu, Rama Krishnan Vasudevan

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42 direct citing works

Designing workflows for materials characterizationOriginalDomain hyper-languages bring robots together and enable the machine learning community4Explainability and human intervention in autonomous scanning probe microscopy17Towards Rapid Autonomous Electron Microscopy with Active Meta-Learning2AEcroscopy: A Software–Hardware Framework Empowering Microscopy Toward Automated and Autonomous Experimentation33Synergizing human expertise and AI efficiency with language model for microscopy operation and automated experiment design *27Integration of scanning probe microscope with high-performance computing: Fixed-policy and reward-driven workflows implementation9Applications of Long Short-Term Memory (LSTM) Networks in Polymeric Sciences: A Review141Automated electron microscopy sample preparation system2Epitaxy of Emerging Materials and Advanced Heterostructures for Microelectronics and Quantum Sciences12Integrating Automated Electrochemistry and High‐Throughput Characterization with Machine Learning to Explore Si─Ge─Sn Thin‐Film Lithium Battery Anodes27Machine Learning to Adaptively Predict Gold Nanorod Sizes on Different Substrates7Leveraging flexible pipette-based tool changes to transform liquid handling systems into dual-function sample preparation and imaging platforms3Machine‐learning‐assisted photonic device development: a multiscale approach from theory to characterization19Designing a unified data structure for solid-electrolyte research projects0Pareto-Optimal Experimentation: Human-Guided Multi-Objective Bayesian Optimization in Scanning Probe Microscopy4Image processing pipeline for AI-driven nanoparticle megalibrary characterization1Wide-field hyperspectral optical microscopy for rapid characterization of two-dimensional semiconductors and heterostructures0Do We Really Need All That Data: From Data to Agency in Automated Microscopy0Emerging frontiers in machine learning-assisted design of functional nanomaterials1Autonomous closed-loop optimization for MAX-phase synthesis via image-based quality evaluation and sequence learning1Multistep Decision Making and Experiment Planning in Automated Microscopy0

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  1. Domain hyper-languages bring robots together and enable the machine learning community

    Device · 2023-10-01

    Shakti P. Padhy, Sergei V. Kalinin

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  2. Explainability and human intervention in autonomous scanning probe microscopy

    Patterns · 2023-10-09

    Yongtao Liu, Maxim A Ziatdinov, Rama Krishnan Vasudevan, Sergei V. Kalinin

    Read original paper ↗ ·

  3. Towards Rapid Autonomous Electron Microscopy with Active Meta-Learning

    Unknown journal · 2023-11-10

    Gayathri Saranathan, Martin Foltín, Aalap Tripathy, Maxim A Ziatdinov, Annmary Justine Koomthanam, Suparna Bhattacharya, Ayana Ghosh, Kevin M. Roccapriore, Sreenivas Rangan Sukumar, Paolo Faraboschi

    Read original paper ↗ ·

  4. AEcroscopy: A Software–Hardware Framework Empowering Microscopy Toward Automated and Autonomous Experimentation

    Small Methods · 2024-04-19

    Yongtao Liu, Kevin M. Roccapriore, Martí Checa, Sai Mani Prudhvi Valleti, Jan‐Chi Yang, Stephen Jesse, Rama Krishnan Vasudevan

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  5. Synergizing human expertise and AI efficiency with language model for microscopy operation and automated experiment design *

    Machine Learning Science and Technology · 2024-05-31

    Yongtao Liu, Martí Checa, Rama Krishnan Vasudevan

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  6. Integration of scanning probe microscope with high-performance computing: Fixed-policy and reward-driven workflows implementation

    Review of Scientific Instruments · 2024-09-01

    Yu Liu, Utkarsh Pratiush, Jason E. Bemis, Roger B. Proksch, Reece Emery, Philip D. Rack, Yuchen Liu, Jan‐Chi Yang, Stanislav A. Udovenko, Susan E. Trolier-McKinstry, Sergei V. Kalinin

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  7. Applications of Long Short-Term Memory (LSTM) Networks in Polymeric Sciences: A Review

    Polymers · 2024-09-14

    Ivan Pavlovich Malashin, В С Тынченко, Andrei Gantimurov, Vladimir Nelyub, А. С. Бородулин

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  8. Automated electron microscopy sample preparation system

    Digital Discovery · 2025-01-01

    David Milsted, Tara Prasad Mishra, Lauren N. Walters, Yuxing Fei, Bernardus Rendy, Pragnay Nevatia, Haegyeom Kim, Gerbrand Ceder

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  9. Epitaxy of Emerging Materials and Advanced Heterostructures for Microelectronics and Quantum Sciences

    Small Methods · 2025-01-07

    Yeonjoo Lee, Soo Ho Choi, Hyunseok Kim, Jinkyoung Yoo

    Read original paper ↗ ·

  10. Integrating Automated Electrochemistry and High‐Throughput Characterization with Machine Learning to Explore Si─Ge─Sn Thin‐Film Lithium Battery Anodes

    Advanced Energy Materials · 2025-01-26

    Alexey O. Sanin, Jackson K. Flowers, Tobias Horst Piotrowiak, Frederic Felsen, Leon Merker, Alfred Ludwig, Dominic Bresser, Helge Sören Stein

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  11. Machine Learning to Adaptively Predict Gold Nanorod Sizes on Different Substrates

    The Journal of Physical Chemistry C · 2025-03-18

    Katsuya Shiratori, Claire A. West, Zhenyang Jia, Stephen Lee, Emily A. Cook, Catherine Jones Murphy, Christy F. Landes, Stephan Link

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  12. Leveraging flexible pipette-based tool changes to transform liquid handling systems into dual-function sample preparation and imaging platforms

    HardwareX · 2025-05-02

    Mohammad Nazeri, Jeffrey Watchorn, Sheldon Mei, Alex Zhang, Christine J. Allen, Frank Gu

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  13. Machine‐learning‐assisted photonic device development: a multiscale approach from theory to characterization

    Nanophotonics · 2025-07-03

    Yuheng Chen, Alexander Montes McNeil, Taehyuk Park, Blake A. Wilson, Vaishnavi Iyer, Michael Bezick, Jae-Ik Choi, Rohan Ojha, Pravin Mahendran, Daksh Kumar Singh, Geetika Chitturi, Peigang Chen, Trang Do, Alexander V. Kildishev, Vladimir M. Shalaev, Michael G. Moebius, Wenshan Cai, Yongmin Liu, Alexandra E. Boltasseva

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  14. Designing a unified data structure for solid-electrolyte research projects

    Science and Technology of Advanced Materials Methods · 2025-10-29

    Yasuhiro Fujiwara, Kota Suzuki, Satoshi Hori, Yoshiyuki Inaguma, Akitoshi Hayashi, Masaki Azuma, Yasutoshi Iriyama, Hirotoshi Yamada, Tetsuhiro Katsumata, Kazunori Takada, Ryoji Kanno, Masahiko Demura

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  15. Pareto-Optimal Experimentation: Human-Guided Multi-Objective Bayesian Optimization in Scanning Probe Microscopy

    Nano Letters · 2025-12-23

    Yu Liu, Sergei V. Kalinin

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  16. Image processing pipeline for AI-driven nanoparticle megalibrary characterization

    Scientific Reports · 2026-02-07

    Alexandra L. Day, Carolin B. Wahl, Roberto dos Reis, Youjia Li, Liao Wei-keng, Vinayak P. Dravid, Chad Alexander Mirkin, Choudhary Alok, Ankit Agrawal

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  17. Wide-field hyperspectral optical microscopy for rapid characterization of two-dimensional semiconductors and heterostructures

    2D Materials · 2026-02-19

    Zhenghan Peng, Adeyemi Uthman, Zhepeng Zhang, Anh Tuấn Hoàng, Xiang Zhu, Eric Pop, Andrew J. Mannix

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  18. Do We Really Need All That Data: From Data to Agency in Automated Microscopy

    Microscopy Today · 2026-03-19

    Sergei V. Kalinin, Kamyar Barakati, Boris N. Slautin, Austin Houston, Utkarsh Pratiush, Gerd J. Duscher, Charles Cao, Ioannis Sgouralis, Steven R. Spurgeon, Charles Yang, Georgia Channing

    Read original paper ↗ ·

  19. Emerging frontiers in machine learning-assisted design of functional nanomaterials

    Next Materials · 2026-03-27

    Avvaru Praveen Kumar, Venkateswarlu V., Maddike Rama Mohana Reddy

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  20. Autonomous closed-loop optimization for MAX-phase synthesis via image-based quality evaluation and sequence learning

    Materials & Design · 2026-06-22

    Jiahui Li, Shengkun Xi, Chao Ouyang, Xiuling Shi, Han Yuan, Rongpei Shi, Junwei Wu, Cuiping Wang, Haijun Zhang, Xiaoyu Chong, Xingjun Liu

    Read original paper ↗ ·

  21. Multistep Decision Making and Experiment Planning in Automated Microscopy

    Microscopy and Microanalysis · 2026-07-01

    Sergei V. Kalinin, Boris N. Slautin, Yu Liu

    Read original paper ↗ ·
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