AXIS Lab mark AXIS Lab
Publications

Research Achievements

CV (PDF) Google Scholar ORCID
01
Peer-reviewed

Journal articles

  2. Oh, S., Roh, J., Park, H., Lee, D., Joo, C. et al.

    Enhancing catalyst performance prediction with hybrid quantum neural networks: a comparative study on data consistency variation

    ACS Sustainable Chemistry & Engineering

  3. Kim, M., Joo, C. co-first , Lim, J. et al.

    Novel inverse predictive system integrated with industrial lubricant information

    Engineering Applications of Artificial Intelligence

  5. Park, H., Joo, C. , Lim, J. et al.

    Novel natural gradient boosting-based probabilistic prediction of physical properties for polypropylene-based composite data

    Engineering Applications of Artificial Intelligence

  6. Joo, C. , Lee, J., Lim, J. et al.

    A genetic algorithm-based optimal selection and blending ratio of plastic waste for maximizing economic potential

    Process Safety and Environmental Protection

  8. Ga, S., An, N., Lee, G., Joo, C. et al.

    Multidisciplinary high-throughput screening of metal–organic framework for ammonia-based green hydrogen production

    Renewable and Sustainable Energy Reviews

  9. Roh, J., Oh, S., Lee, D., Joo, C. et al.

    Hybrid quantum neural network model with catalyst experimental validation: application for the dry reforming of methane

    ACS Sustainable Chemistry & Engineering

  14. Ga, S., An, N., Joo, C. co-first et al.

    pyAPEP: an all-in-one software package for the automated preparation of adsorption process simulations

    Computer Physics Communications

  15. Jeong, S., Joo, C. co-first , Lim, J., Cho, H. et al.

    A novel graph-based missing values imputation method for industrial lubricant data

    Computers in Industry

  19. Lee, J., Joo, C. co-first , Cho, H., Kim, Y. et al.

    Design of multistage fixed bed reactors for SMR hydrogen production based on the intrinsic kinetics of Ru-based catalysts

    Energy Conversion and Management

  20. Joo, C. , Park, H., Lim, J., Cho, H. et al.

    Development of physical property prediction models for polypropylene composites with optimizing random forest hyperparameters

    International Journal of Intelligent Systems

Conference presentations

Selected oral presentations

Temperature-conditioned data-driven modeling for organic solubility prediction
The Society of Adhesion and Interface, Korea

Chemical Property-Guided Neural Networks for Naphtha Composition Prediction
IEEE INDIN, Germany

Data-Driven Modeling to Predict the Physical Properties of the Lubricant
AIChE Annual Meeting, Phoenix, USA

Intellectual property

Patent applications

Three patent applications covering blended absorbent prediction, hybrid quantum neural networks for catalyst performance, and machine-learning-based lubricant recipe recommendation.