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RESEARCH PAPER · MRS Bulletin · 2022

AutoMat: Automated materials discovery for electrochemical systems

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

Open in your workspace →Saved 2026-10-14 · complete coverage
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42 direct citing works

AutoMat: Automated materials discovery for electrochemical systemsOriginalDriving school for self-driving labs17Towards informatics-driven design of nuclear waste forms11Machine learning in materials research: Developments over the last decade and challenges for the future105Differentiable modeling and optimization of non-aqueous Li-based battery electrolyte solutions using geometric deep learning40Recent Advancements in Applying Machine Learning in Power-to-X Processes: A Literature Review27Accelerating climate technologies through the science of scale-up4A data driven investigation of photostability and formation energies in arsenic-containing compounds: machine learning-gaussian process output analysis2Automatic Generation of Chemical Mechanisms for Electrochemical Systems: Solid Electrolyte Interphase Formation in Lithium Batteries5Benchmarking self-driving labs13Integrating computational chemistry and machine learning for the stability prediction of transition metal ternary compounds4AI meets high-entropy alloys: From classification and regression to explainable models and predictive discovery1Bayesian Optimisation Against Climate Change: Applications and Benchmarks1Machine Learning in Materials Science: Current Challenges and Future Outlook0Tokenization for Molecular Foundation Models4Benchtop Autonomous Electrochemical Characterization System for Combinatorial Thin-Film Solid Oxide Electrodes2CLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation Learning1

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  1. Driving school for self-driving labs

    Digital Discovery · 2023-01-01

    Kelsey L. Snapp, Keith A. Brown

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  2. Towards informatics-driven design of nuclear waste forms

    Digital Discovery · 2024-01-01

    Vinay I. Hegde, Miroslava Peterson, Sarah I. Allec, Xiaonan Lu, Thiruvillamalai Mahadevan, Thanh Son Nguyen, Jayani Kalahe, Jared M. Oshiro, Robert J. Seffens, Ethan K. Nickerson, Jincheng Du, Brian Joseph Riley, John D. Vienna, James E. Saal

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  3. Machine learning in materials research: Developments over the last decade and challenges for the future

    Current Opinion in Solid State and Materials Science · 2024-09-11

    Anubhav Jain

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  4. Differentiable modeling and optimization of non-aqueous Li-based battery electrolyte solutions using geometric deep learning

    Nature Communications · 2024-10-05

    Shang Zhu, Bharath Ramsundar, Emil Jonathan Annevelink, Hongyi Lin, Adarsh Dave, Pin-Wen Guan, Kevin L. Gering, Venkatasubramanian Viswanathan

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  5. Recent Advancements in Applying Machine Learning in Power-to-X Processes: A Literature Review

    Sustainability · 2024-11-02

    S. Mohammad Shojaei, Reihaneh Aghamolaei, Mohammad Reza Ghaani

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  6. Accelerating climate technologies through the science of scale-up

    Nature Chemical Engineering · 2024-12-18

    Thomas Moore, Andrew Allen Wong, Brian Giera, Diego I. Oyarzun, Aldair E. Gongora, Tiras Y. Lin, Wenqin Li, Tracie Owens, Du Thai Nguyen, Victoria Marie Ehlinger, Aditya Prajapati, Seung Whan Chung, Pratanu Roy, Joshua R. DeOtte, Nicholas R. Cross, Alvina Aui, Youngsoo Choi, Maxwell Goldman, Hui‐Yun Jeong, Congwang Ye, Amitava Sarkar, Eric B. Duoss, Christopher Hahn, Sarah E. Baker

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  7. A data driven investigation of photostability and formation energies in arsenic-containing compounds: machine learning-gaussian process output analysis

    Materials Chemistry and Physics · 2025-06-09

    Hussein Ali Kadhim Kyhoiesh, Azal S. Waheeb, Karrar Hazim Salem, Ashraf Y. Elnaggar, Islam H. El Azab, M.H.H. Mahmoud, Dunya Jameel Kassid, H. Salman

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  8. Automatic Generation of Chemical Mechanisms for Electrochemical Systems: Solid Electrolyte Interphase Formation in Lithium Batteries

    The Journal of Physical Chemistry C · 2025-07-07

    Matthew S. Johnson, David S. Farina, Lance Kavalsky, Judit Zádor, Richard H. West, Venkatasubramanian Viswanathan, William H. Green

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  9. Benchmarking self-driving labs

    Digital Discovery · 2025-10-09

    Adedire D. Adesiji, Jiashuo Wang, Cheng-Shu Kuo, Keith A. Brown

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  10. Integrating computational chemistry and machine learning for the stability prediction of transition metal ternary compounds

    Materials Chemistry and Physics · 2025-11-04

    Afaf Murtadha Kadhum, Azal S. Waheeb, Shaimaa Hassan Mallah, Masar Ali Awad, Duha M. Hasan, Hussein Ali Kadhim Kyhoiesh, Hassan E. Abd Elsalam, Islam H. El Azab

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  11. AI meets high-entropy alloys: From classification and regression to explainable models and predictive discovery

    Elsevier eBooks · 2026-01-01

    Stéphane Gorsse, Wen‐Jay Lee

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  12. Bayesian Optimisation Against Climate Change: Applications and Benchmarks

    Lecture notes in computer science · 2026-01-01

    Sigrid Passano Hellan, Christopher G. Lucas, Nigel Goddard

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  13. Machine Learning in Materials Science: Current Challenges and Future Outlook

    Springer eBooks · 2026-01-01

    Amit Bhaskar, Pankaj Yadav, Brihaspati Singh, Saurabh Kumar Singh, Sunil Singh Rana

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  14. Tokenization for Molecular Foundation Models

    Journal of Chemical Information and Modeling · 2026-01-23

    Alexius Wadell, Anoushka Bhutani, Venkatasubramanian Viswanathan

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  15. Benchtop Autonomous Electrochemical Characterization System for Combinatorial Thin-Film Solid Oxide Electrodes

    PRX Energy · 2026-02-09

    Jake Huang, Meagan C. Papac, Davi Fébba, Ryan O’Hayre, Andriy Zakutayev

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  16. CLOUD: A Scalable and Physics-Informed Foundation Model for Crystal Representation Learning

    Nature Communications · 2026-03-17

    Changwen Xu, Shang Zhu, Venkatasubramanian Viswanathan

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