About the Research
Research Theme
Graph Network-based Simulators for Complex Reaction Networks, 3D Visualizations, Foundation Models for Chemistry
Keyword
Research Outline
My research lies in the interdisciplinary fields of computational science and informatics, with a primary focus on developing cutting-edge geometric deep learning (GDL) models guided by physical principles. These models are applied to learn and simulate complex chemical reaction paths and networks. Additionally, I am particularly interested in developing representation tools for complex reaction networks through 3D visualization techniques (such as three.js) and augmenting GDL models with reinforcement learning techniques, such as world models. I have also been involved in several external collaborative projects focusing on building large foundation models for chemistry.
Prior to joining WPI-ICReDD, I gained rich experience in developing geometric-based descriptors and machine learning potentials (MLPs) by leveraging datasets from multiscale modeling for physical and chemical property predictions, in alignment with the AI4Science community.
Representative Research Achievements
- Developing Cheap but Useful Machine Learning-Based Models for Investigating High-Entropy Alloy Catalysts
Chenghan Sun, R. Goel, Ambarish R. Kulkarni. Langmuir, 2024, 40, 7, 3691–3701.
DOI: 10.1021/acs.langmuir.3c03401 - Elucidating the Fluxionality and Dynamics of Zeolite-Confined Gold Nanoclusters Using Machine Learning Potentials
Siddharth Sonti, Chenghan Sun (co-first author), Zekun Chen, Robert M. Kowalski, Joseph S. Kowalski, Davide Donadio, Surl-Hee Ahn, Ambarish R Kulkarni.
Pending submission to Journal of the American Chemical Society
ChemRxiv version: https://chemrxiv.org/engage/chemrxiv/article-details/6552db546e0ec7777fe3a056 - Screening Cu-Zeolites for Methane Activation Using Curriculum-Based Training
Jiawei Guo, Tyler Sours, Sam Holton, Chenghan Sun, Ambarish R. Kulkarni.
ACS Catal., 2024, 14, 3, 1232–1242.
DOI: 10.1021/acscatal.3c05275 - Efficient Prediction of Partial Charges with a Size Extensive Multi-objective Deep Neural Network
Wang-Yeuk Kong, Chenghan Sun (co-first author), Zekun Chen, Dean J. Tantillo, Davide Donadio.
Manuscript in preparation