Kewei Zhu

Happy Researcher

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About me

I'm a PhD student under the supervision of Prof. Peyman Z Moghadam and Prof. Mojtaba Abdi-Jalebi at University College London (UCL) since 2023.

I am passionate about applying machine learning to advance molecular research, especially in Metal-Organic Frameworks (MOFs). My focus includes:

  • Data-Driven Quantitative Structure-Property Relationships (QSPR): Predicting structure-property relationships for efficient material discovery.
  • Generative AI for material design: Using deep learning and large language models (LLMs) to create novel MOFs with tailored properties.
    • My puppy QQ and me want bubble tea

    Timelines

    • April 2025

      🌟 Honoured to be invited as a speaker at the British Zeolite Association (BZA) 2025 Annual Meeting! 🎤🧪 Huge thanks to the amazing organizers 🙌, generous sponsors 💼, and Cardiff University 🏛️ for hosting this inspiring event. It was a fantastic experience sharing our latest work on porous materials — including zeolites 🧱 and MOFs 🧊 — and connecting with brilliant minds in the field! 🔬💡🤝

    • 17 Mar 2025

      I'm thrilled to share that my registration has been officially upgraded from MPhil stage to PhD stage! 🎉 A huge thank you to everyone who has supported me throughout this journey.✨

    • Oct 2024 - Feb 2025

      🚀 Come and join the XtalPi Innovation Center! Be part of our 🌟 Technology Innovation Department, driving groundbreaking computation R&D. Let’s shape the future together! 🌐✨

    • Sep 2024

      Delighted to attend the I-X Breaking Topics in AI Conference 🤖✨, where I engaged with thought-provoking presentations 📊 and dynamic discussions on AI4Science 🔬🚀.

    • Jul/Aug 2024

      Thrilled 🎉 to serve as a reviewer for 🤖Neural Information Processing Systems (NeurIPS 2024), contributing to cutting-edge AI research.

    • Jul 2024

      Excited to attend the ☀️International Conference on Machine Learning (ICML 2024) in Vienna🇦🇹, connecting with global experts 🌏 and exploring the latest advancements in machine learning.

    • Jun 2024

      Grateful to participate in the second Workshop on Multimodal AI in Sheffield🇬🇧, expanding my knowledge and network in cutting-edge 🤝 multimodal AI research.

    • Jan 2024

      Exciting opportunity to attend the ❄️Molsim-2024 winter school in Amsterdam🇳🇱, deepening my expertise in molecular simulation techniques.

    • Sep 2023

      Begin my PhD journey, focusing on 🤖 machine learning applications in ⚛️ metal-organic frameworks (MOFs) for CO₂ capture 🌳🌳🌳.

    Publications

    Main Works

    • Analyzing drop coalescence in microfluidic devices with a deep learning generative model

      Authors: Kewei Zhu, Sibo Cheng, Nina Kovalchuk, Mark Simmons, Yi-Ke Guo, Omar K Matar, Rossella Arcucci

      Physical Chemistry Chemical Physics (PCCP)

      Paper | Arxiv | Code | BibTex

      Thumbnail of Paper Title 1

    • Explainable AI models for predicting drop coalescence in microfluidics device

      Authors: Jinwei Hu, Kewei Zhu, Sibo Cheng, Nina M Kovalchuk, Alfred Soulsby, Mark JH Simmons, Omar K Matar, Rossella Arcucci

      Chemical Engineering Journal (CEJ)

      Paper | Code | BibTex

      Thumbnail of Paper Title 1

    Cooperations

      Accurate identification and measurement of the precipitate area by two-stage deep neural networks in novel chromium-based alloys

      Authors: Zeyu Xia, Kan Ma, Sibo Cheng, Thomas Blackburn, Ziling Peng, Kewei Zhu, Weihang Zhang, Dunhui Xiao, Alexander J Knowles, Rossella Arcucci

      Physical Chemistry Chemical Physics (PCCP)

      Paper | Code | BibTex

      Thumbnail of Paper Title 1

      Augmented Reality for Enhanced Visualization of MOF Adsorbents

      Authors: Lawson T Glasby, Rama Oktavian, Kewei Zhu, Joan L Cordiner, Jason C Cole, Peyman Z Moghadam

      Journal of Chemical Information and Modeling (JCIM)

      Paper | BibTex

      Thumbnail of Paper Title 1

    Reviews

    • Machine learning and physics-driven modelling and simulation of multiphase systems

      Authors: Nausheen Basha, Rossella Arcucci, Panagiota Angeli, Charitos Anastasiou, Thomas Abadie, César Quilodrán Casas, Jianhua Chen, Sibo Cheng, Loïc Chagot, Federico Galvanin, Claire E Heaney, Fria Hossein, Jinwei Hu, Nina Kovalchuk, Maria Kalli, Lyes Kahouadji, Morgan Kerhouant, Alessio Lavino, Fuyue Liang, Konstantia Nathanael, Luca Magri, Paola Lettieri, Massimiliano Materazzi, Matteo Erigo, Paula Pico, Christopher C Pain, Mosayeb Shams, Mark Simmons, Tullio Traverso, Juan Pablo Valdes, Zef Wolffs, Kewei Zhu, Yilin Zhuang, Omar K Matar

      International Journal of Multiphase Flow

      Paper | BibTex

    • Machine learning for modelling unstructured grid data in computational physics: a review

      Authors: Sibo Cheng, Marc Bocquet, Weiping Ding, Tobias Sebastian Finn, Rui Fu, Jinlong Fu, Yike Guo, Eleda Johnson, Siyi Li, Che Liu, Eric Newton Moro, Jie Pan, Matthew Piggott, Cesar Quilodran, Prakhar Sharma, Kun Wang, Dunhui Xiao, Xiao Xue, Yong Zeng, Mingrui Zhang, Hao Zhou, Kewei Zhu, Rossella Arcucci

      Arxiv | BibTex

    Key Cooperators