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RESEARCH PAPER · The Journal of Chemical Physics · 2022

Active meta-learning for predicting and selecting perovskite crystallization experiments

Venkateswaran Shekar, Gareth Nicholas, Mansoor Ani Najeeb, Margaret Zeile, Vincent Yu, Xiaorong Wang, Dylan Slack, Zhi Li, Philip W. Nega, Emory M. Chan, Alexander J. Norquist, Joshua Schrier, Sorelle A. Friedler

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

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42 direct citing works

Active meta-learning for predicting and selecting perovskite crystallization experimentsOriginalApplications of Artificial Intelligence and Machine Learning Algorithms to Crystallization160Serendipity based recommender system for perovskites material discovery: balancing exploration and exploitation across multiple models7Serendipity based recommender system for perovskites material discovery: balancing exploration and exploitation across multiple models5Improving the predictive power of microkinetic models via machine learning15Chemical design by artificial intelligence5Collaborative methods to enhance reproducibility and accelerate discovery20Integrating autonomy into automated research platforms32Research on Damage Prediction Methods for Service Turbine Blades Based on Meta-Learning1Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects169The role of machine learning in perovskite solar cell research42Machine Learning‐Accelerated Development of Perovskite Optoelectronics Toward Efficient Energy Harvesting and Conversion3Accelerated room temperature synthesis of desired cesium lead halide perovskite nanocrystals via automated microfluidic meta learner4In Pursuit of the Exceptional: Research Directions for Machine Learning in Chemical and Materials Science99Role of multifidelity data in sequential active learning materials discovery campaigns: case study of electronic bandgap7Development and application of Few-shot learning methods in materials science under data scarcity47Repeatable Perovskite Solar Cells through Fully Automated Spin-Coating and Quenching21Unraveling the myths and mysteries of photon avalanching nanoparticles16Advancing extrapolative predictions of material properties through learning to learn using extrapolative episodic training13Perovskite solar cells empowered by machine learning16Progress of Machine Learning in Molecular Crystal Design and Crystallization Development11Beyond Optimization: Exploring Novelty Discovery in Autonomous Experiments10Recent advances in the digital transformation of crystallization process development and operation: Synergy between model- and AI-driven strategies4Artificial Intelligence for Perovskite Additive Engineering: From Molecular Screening to Autonomous Discovery1Leveling Up Upconverting Nanoparticles with Machine Learning4

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24 direct citing papers saved.

  1. Applications of Artificial Intelligence and Machine Learning Algorithms to Crystallization

    Chemical Reviews · 2022-06-27

    Christos Xiouras, Fabio Cameli, Gustavo Lunardon Quilló, Michail E. Kavousanakis, Dionisios G. Vlachos, Georgios D. Stefanidis

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  2. Serendipity based recommender system for perovskites material discovery: balancing exploration and exploitation across multiple models

    ChemRxiv · 2022-07-19

    Venkateswaran Shekar, Vincent Yu, Benjamin J. Garcia, David Benjamin Gordon, Gemma E. Moran, David M. Blei, Loı̈c M. Roch, Alberto García-Durán, Mansoor Ani Najeeb, Margaret Zeile, Philip W. Nega, Zhi Li, Mina A. Kim, Emory M. Chan, Alexander J. Norquist, Sorelle A. Friedler, Joshua Schrier

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  3. Serendipity based recommender system for perovskites material discovery: balancing exploration and exploitation across multiple models

    ChemRxiv · 2022-07-20

    Venkateswaran Shekar, Vincent Yu, Benjamin J. Garcia, David Benjamin Gordon, Gemma E. Moran, David M. Blei, Loı̈c M. Roch, Alberto García-Durán, Mansoor Ani Najeeb, Margaret Zeile, Philip W. Nega, Zhi Li, Mina A. Kim, Emory M. Chan, Alexander J. Norquist, Sorelle A. Friedler, Joshua Schrier

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  4. Improving the predictive power of microkinetic models via machine learning

    Current Opinion in Chemical Engineering · 2022-08-26

    Srinivas Rangarajan, Huijie Tian

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  5. Chemical design by artificial intelligence

    The Journal of Chemical Physics · 2022-09-28

    Daniel H. Ess, Kim E. Jelfs, Heather J. Kulik

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  6. Collaborative methods to enhance reproducibility and accelerate discovery

    Digital Discovery · 2022-11-29

    Drew A. Leins, Steven B. Haase, Mohammed Eslami, Joshua Schrier, Jared Freeman

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  7. Integrating autonomy into automated research platforms

    Digital Discovery · 2023-01-01

    Richard B. Canty, Brent A. Koscher, Matthew A. McDonald, Klavs F. Jensen

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  8. Research on Damage Prediction Methods for Service Turbine Blades Based on Meta-Learning

    SSRN Electronic Journal · 2023-01-01

    Dongwei Li, Ning Li, Jinxiang Liu, Weiqing Huang, Kaiheng Sun

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  9. Machine Learning for Perovskite Solar Cells and Component Materials: Key Technologies and Prospects

    Advanced Functional Materials · 2023-02-15

    Yiming Liu, Xinyu Tan, Jie Liang, Hongwei Han, Peng Xiang, Wensheng Yan

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  10. The role of machine learning in perovskite solar cell research

    Journal of Alloys and Compounds · 2023-06-01

    Chen Chen, Ayman Maqsood, T. Jesper Jacobsson

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  11. Machine Learning‐Accelerated Development of Perovskite Optoelectronics Toward Efficient Energy Harvesting and Conversion

    Advanced Energy and Sustainability Research · 2023-08-30

    Baian Chen, Rui Chen, Bolong Huang

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  12. Accelerated room temperature synthesis of desired cesium lead halide perovskite nanocrystals via automated microfluidic meta learner

    Chemical Engineering Science · 2023-09-27

    Yijian Yin, Jun-Xuan Jin, Gaopeng Ren, Ke‐Jun Wu, Chao‐Hong He

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  13. 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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  14. Role of multifidelity data in sequential active learning materials discovery campaigns: case study of electronic bandgap

    Machine Learning Science and Technology · 2023-12-01

    Ryan M. Jacobs, Philip E. Goins, Dane D. Morgan

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  15. Development and application of Few-shot learning methods in materials science under data scarcity

    Journal of Materials Chemistry A · 2024-01-01

    Yongxing Chen, Long Peng, Bin Liu, Yi Wang, Junlong Wang, Tian Ma, Huilin Wei, Yue Kang, Haining Ji

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  16. Repeatable Perovskite Solar Cells through Fully Automated Spin-Coating and Quenching

    ACS Applied Materials & Interfaces · 2024-09-30

    Daniel O. Baumann, Felix Laufer, Julie Roger, Roja Singh, Mohammad Gholipoor, Ulrich W. Paetzold

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  17. Unraveling the myths and mysteries of photon avalanching nanoparticles

    Materials Horizons · 2025-01-01

    Artiom Skripka, Emory M. Chan

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  18. Advancing extrapolative predictions of material properties through learning to learn using extrapolative episodic training

    Communications Materials · 2025-02-22

    Kohei Noda, Araki Wakiuchi, Yoshihiro Hayashi, Ryo Yoshida

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  19. Perovskite solar cells empowered by machine learning

    Journal of Energy Chemistry · 2025-05-26

    Zheng Li, Chong Xing Huang, Chong Huang, Lingfeng Chao, Yonghua Chen, Wei Huang, Gaojie Chen

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  20. Progress of Machine Learning in Molecular Crystal Design and Crystallization Development

    Engineering · 2025-06-04

    Shengzhe Jia, Yiming Ma, Yuechao Cao, Zhenguo Gao, Sohrab Rohani, Junbo Gong, Jingkang Wang

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  21. Beyond Optimization: Exploring Novelty Discovery in Autonomous Experiments

    ACS Nanoscience Au · 2025-10-14

    Ralph Bulanadi, Jawad Chowdhury, Hiroshi Funakubo, Maxim A Ziatdinov, Rama Krishnan Vasudevan, Arpan Biswas, Yongtao Liu

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  22. Recent advances in the digital transformation of crystallization process development and operation: Synergy between model- and AI-driven strategies

    Particuology · 2026-01-23

    Yiming Ma, Xuming Yuan, Ashish Yewale, Brahim Benyahia

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  23. Artificial Intelligence for Perovskite Additive Engineering: From Molecular Screening to Autonomous Discovery

    Molecules · 2026-01-27

    Xin-De Wang, Zhi-Rui Chen, Wen-Kao Li, Peng‐Jie Guo, Cheng Mu, Ze-Feng Gao, Zhong-Yi Lu

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  24. Leveling Up Upconverting Nanoparticles with Machine Learning

    Accounts of Chemical Research · 2026-05-26

    Ripeng Luo, Jungmin Hamm, Emory M. Chan

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