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RESEARCH PAPER · npj Computational Materials · 2021

Autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materials

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42 direct citing works

Autonomous reinforcement learning agent for chemical vapor deposition synthesis of quantum materialsOriginalInterfacial informatics4Functional Nanomaterials Design in the Workflow of Building Machine-Learning Models1Learning time-dependent deposition protocols to design thin films via genetic algorithms10Low temperature CVD growth of WSe 2 enabled by moisture-assisted defects in the precursor powder14Atomistic reaction mechanism of CVD grown MoS2 through MoO3 and H2S precursors23Modeling materials under coupled extremes: Enabling better predictions of performance4Discovering mechanisms for materials microstructure optimization via reinforcement learning of a generative model8Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation118What is missing in autonomous discovery: open challenges for the community44Wrinkles, Ridges, Miura-Ori, and Moiré Patterns in MoSe2 Using Neural Networks6AlphaFlow: autonomous discovery and optimization of multi-step chemistry using a self-driven fluidic lab guided by reinforcement learning215Tailoring the Angular Mismatch in MoS2 Homobilayers through Deformation Fields6Reinforcement learning algorithms: A brief survey656Quantitative Topic Analysis of Materials Science Literature Using Natural Language Processing24Machine learning approaches for screening of materials in flexible electronic devices0Beyond Combinatorial Materials Science: The 100 Prisoners Problem9Recent advances in artificial intelligence boosting materials design for electrochemical energy storage124Molecular Autonomous Pathfinder Using Deep Reinforcement Learning2Probing phonon focusing, thermomechanical behavior, and moiré patterns in van der Waals architectures using surface acoustic waves3A data-driven method to construct prediction model of solar stills7Deep reinforcement learning for inverse inorganic materials design59Colloidal systems as experimental platforms for physics-informed machine learning2Generative AI for design of nanoporous materials: review and future prospects41Artificial intelligence interventions in 2D MXenes-based photocatalytic applications38How to accelerate the inorganic materials synthesis: from computational guidelines to data-driven method?9Artificial intelligence-empowered functional design of semi-transparent optoelectronic and photonic devices via deep Q-learning14Multi‐source data‐driven model to predict the deposition rate of CVD‐SiC coatings with improved accuracy and robustness3Recent Representative Progress of Surface Coating Technology28Reinforcement Learning Approach to Advance Non‐Fullerene Acceptor‐Based Semi‐Transparent Organic Solar Cell7Chemistry3D: Robotic Interaction Toolkit for Chemistry Experiments0A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction5Data-driven microstructural optimization of Ag-Bi-I perovskite-inspired materials10Advances in intelligent epitaxy of semiconductor materials1AI-driven design of powder-based nanomaterials for smart textiles: from data intelligence to system integration27Artificial intelligence in chemical reaction engineering0Multi-stage Bayesian optimisation for dynamic decision-making in self-driving labs1Agentic material science5Machine Learning as a Catalyst in Advanced Materials Research and Development0From green routes to digital design: Two-dimensional photocatalysts at the interface of machine learning and environmental applications0Neural network framework for predicting deposition thickness and electrical resistance in printed electronics1Sustainable Materials Design With Multi‐Modal Artificial Intelligence1A survey of machine learning applications for Mg alloys: A promising prospect for current trends and future directions2CA-SMART: An active learning framework for accelerating materials discovery under resource constraints1Artificial intelligence for carbon materials in electrochemical energy storage: Methods, applications, and perspectives0Reinforcement Learning for Cathode Material Design Through Sequential Decision-Making Frameworks0

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  1. Interfacial informatics

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  2. Functional Nanomaterials Design in the Workflow of Building Machine-Learning Models

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  3. Learning time-dependent deposition protocols to design thin films via genetic algorithms

    Materials & Design · 2022-06-06

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  4. Low temperature CVD growth of WSe 2 enabled by moisture-assisted defects in the precursor powder

    2D Materials · 2022-08-31

    Lucas M. Sassi, Aravind Krishnamoorthy, Jordan A. Hachtel, Sandhya Susarla, Amey Apte, Samuel Castro Pardo, Alec Ajnsztajn, Róbert Vajtai, Priya D. Vashishta, Chandra Sekhar Tiwary, Anand B. Puthirath, Pulickel Madhavapanicker Ajayan

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  5. Atomistic reaction mechanism of CVD grown MoS2 through MoO3 and H2S precursors

    Scientific Reports · 2022-09-27

    Abdullah Arafat, Md. Sherajul Islam, Naim Ferdous, A. S. M. Jannatul Islam, Md. Mosarof Hossain Sarkar, Catherine Stampfl, Jeongwon Park

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  6. Modeling materials under coupled extremes: Enabling better predictions of performance

    MRS Bulletin · 2022-11-01

    Aaron A. Kohnert, Brian D. Wirth, Chris M. Wolverton, Prasanna V. Balachandran, Laurent Capolungo

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  7. Discovering mechanisms for materials microstructure optimization via reinforcement learning of a generative model

    Machine Learning Science and Technology · 2022-11-03

    Rama Krishnan Vasudevan, Erick Orozco, Sergei V. Kalinin

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  8. Toward autonomous laboratories: Convergence of artificial intelligence and experimental automation

    Progress in Materials Science · 2022-11-11

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  9. What is missing in autonomous discovery: open challenges for the community

    Digital Discovery · 2023-01-01

    Phillip M. Maffettone, Pascal Friederich, Sterling G. Baird, Ben J. Blaiszik, Keith A. Brown, Stuart I. Campbell, Orion Cohen, Rebecca L. Davis, Ian T Foster, Navid Haghmoradi, Mark Hereld, Howie Joress, Nicole Jung, Ha-Kyung Kwon, Gabriella Pizzuto, Jacob Rintamaki, Casper Steinmann, Luca Torresi, Shijing Sun

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  10. Wrinkles, Ridges, Miura-Ori, and Moiré Patterns in MoSe2 Using Neural Networks

    The Journal of Physical Chemistry Letters · 2023-02-09

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  11. AlphaFlow: autonomous discovery and optimization of multi-step chemistry using a self-driven fluidic lab guided by reinforcement learning

    Nature Communications · 2023-03-14

    Amanda A. Volk, Robert W. Epps, Daniel T. Yonemoto, Benjamin S. Masters, Felix N. Castellano, Kristofer G. Reyes, Milad Abolhasani

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  12. Tailoring the Angular Mismatch in MoS2 Homobilayers through Deformation Fields

    Small · 2023-04-07

    Kory Burns, Anne Marie Z. Tan, Jordan A. Hachtel, Anikeya Aditya, Nitish Baradwaj, Ankit Mishra, Thomas Linker, Aiichiro Nakano, Rajiv K. Kalia, Eric J. Lang, Ryan M. Schoell, Richard G. Hennig, Khalid Hattar, Assel Aitkaliyeva

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  13. Reinforcement learning algorithms: A brief survey

    Expert Systems with Applications · 2023-05-23

    Ashish Kumar Shakya, G. N. Pillai, Sohom Chakrabarty

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  14. Quantitative Topic Analysis of Materials Science Literature Using Natural Language Processing

    ACS Applied Materials & Interfaces · 2023-12-07

    Jaewoong Choi, Byungju Lee

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  15. Machine learning approaches for screening of materials in flexible electronic devices

    Unknown journal · 2024-01-01

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  16. Beyond Combinatorial Materials Science: The 100 Prisoners Problem

    Integrating materials and manufacturing innovation · 2024-01-08

    James Elliott Fowler, Matthew A. Kottwitz, Nathaniel Trask, Rémi Dingreville

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  17. Recent advances in artificial intelligence boosting materials design for electrochemical energy storage

    Chemical Engineering Journal · 2024-04-23

    X.-B. Liu, Kexin Fan, Xinmeng Huang, Jiankai Ge, Yujie Liu, Haisu Kang

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  18. Molecular Autonomous Pathfinder Using Deep Reinforcement Learning

    The Journal of Physical Chemistry Letters · 2024-05-09

    Ken‐ichi Nomura, Ankit Mishra, Tian Sang, Rajiv K. Kalia, Aiichiro Nakano, Priya D. Vashishta

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  19. Probing phonon focusing, thermomechanical behavior, and moiré patterns in van der Waals architectures using surface acoustic waves

    npj Computational Materials · 2024-06-28

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  20. A data-driven method to construct prediction model of solar stills

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    Senshan Sun, Juxin Du, Guilong Peng, Nuo Yang

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  21. Deep reinforcement learning for inverse inorganic materials design

    npj Computational Materials · 2024-12-19

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  22. Colloidal systems as experimental platforms for physics-informed machine learning

    Nanoscale Horizons · 2025-01-01

    Namhee Kang, Yeonseo Joo, Hyosung An, Hyerim Hwang

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  23. Generative AI for design of nanoporous materials: review and future prospects

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    Evan Xie, Xijun Wang, Joern Ilja Siepmann, Haoyuan Chen, Randall Q. Snurr

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  24. Artificial intelligence interventions in 2D MXenes-based photocatalytic applications

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  25. How to accelerate the inorganic materials synthesis: from computational guidelines to data-driven method?

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  26. Artificial intelligence-empowered functional design of semi-transparent optoelectronic and photonic devices via deep Q-learning

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  27. Multi‐source data‐driven model to predict the deposition rate of CVD‐SiC coatings with improved accuracy and robustness

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  28. Recent Representative Progress of Surface Coating Technology

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  29. Reinforcement Learning Approach to Advance Non‐Fullerene Acceptor‐Based Semi‐Transparent Organic Solar Cell

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  31. A Machine Learning Model for Interpretable PECVD Deposition Rate Prediction

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  32. Data-driven microstructural optimization of Ag-Bi-I perovskite-inspired materials

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  35. Artificial intelligence in chemical reaction engineering

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  37. Agentic material science

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  38. Machine Learning as a Catalyst in Advanced Materials Research and Development

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