Chen Guzhong is a motivated researcher in the field of Chemical Engineering. He obtained his Bachelor (2014) and Ph.D. (2023) from East China University of Science and Technology, under the supervision of Prof. Dr.-Ing. Zhiwen Qi. From 2021 to 2023, he also participated in a joint Ph.D. program at the Max Planck Institute for Complex Dynamic Systems, supervised by Prof. Dr.-Ing. Kai Sundmacher. Dr. Chen's research focuses on advancing the field of molecule and material design by developing innovative machine learning-based methods and tools. His work enables rapid and accurate predictions of molecular properties and facilitates high-throughput screening of customized molecules. During his doctoral studies, he developed several cutting-edge models and frameworks, including a deep neural network-based recommender system for predicting infinite dilution activity coefficients, a deep learning model for predicting σ-profiles and VCOSMO, and a one-stop ionic liquid representation transfer learning model framework. He also designed an automated machine learning modeling framework to streamline the construction of molecular property prediction models. His research contributions have advanced the development of machine learning-based molecule design methods, addressing challenges related to limited labeled training datasets, computational overhead, and time-consuming modeling processes. With his background in chemical engineering, machine learning, and programming, Dr. Chen is well-equipped to tackle complex problems in the field of molecule and material design. He is committed to further exploring the intersection of machine learning and chemical engineering to develop innovative solutions for real-world challenges.
Publications
[1] Chen, G., Song, Z., Qi, Z., Sundmacher, K. (2021). Neural recommender system for
the activity coefficient prediction and UNIFAC model extension of ionic liquid-solute
systems. AIChE Journal, 67(4), e17171.
[2] Chen, G., Song, Z., Qi, Z. (2021). Transformer-convolutional neural network for
surface charge density profile prediction: Enabling high-throughput solvent screening with
COSMO-SAC. Chemical Engineering Science, 246, 117002.
[3] Chen, G., Song, Z., Qi, Z., Sundmacher, K. (2023). Generalizing property prediction
of ionic liquids from limited labeled data: a one-stop framework empowered by transfer
learning. Digital Discovery, 2, 591-601.
[4] Chen, G., Song, Z., Qi, Z., Sundmacher, K. (2023). A Scalable and Integrated
Machine Learning Framework for Molecular Properties Prediction. AIChE Journal,
69(10), e18185.
[5] Wang, R., Chen, G., Qin, H., Cheng, H., Chen, L., Qi, Z. (2021). Systematic
screening of bifunctional ionic liquid for intensifying esterification of methyl heptanoate in
the reactive extraction process. Chemical Engineering Science, 246, 116888.
[6] Tan, T., Cheng, H., Chen, G., Song, Z., Qi, Z. (2022). Prediction of infinite-dilution
activity coefficients with neural collaborative filtering. AIChE Journal, 68(9), e17789.
[7] Song, Z., Chen, J., Cheng, J., Chen, G., Qi, Z. (2024). Computer-aided molecular
design of ionic liquids as advanced process media: A review from fundamentals to
applications. Chemical Reviews, 124, 2, 248–317.