Postdoctoral Researcher · SIMIT, Chinese Academy of Sciences
I build AI for materials science.
I'm a postdoctoral researcher at the Shanghai Institute of Microsystem and Information Technology (SIMIT), Chinese Academy of Sciences, working at the intersection of deep learning, physics-informed modeling, and semiconductor materials.
Move the pointer to perturb the lattice
Working with Prof. Wenjie Yu under the Shanghai Super Postdoc Program, on AI-driven materials research.
I am a postdoctoral researcher at SIMIT, CAS. I received my Ph.D. in Microelectronics and Solid-State Electronics from SIMIT, CAS in 2025.
Prior to that, I earned an M.Sc. in Computer Science and Artificial Intelligence from the University of Manchester (2021) and a B.Sc. from the University of Nottingham Ningbo China (2020).
From July 2024 to March 2025, I was a visiting researcher at the RIKEN Center for Advanced Intelligence Project (AIP), Japan, collaborating with Dr. Qibin Zhao.
Materials carry intrinsic properties across modalities and scales that pure data-driven approaches struggle to capture. My work integrates physical mechanisms into representation learning — fusing molecular sequences, graph structures, 3D conformations, and domain knowledge into unified multimodal representations. Physical priors are embedded in downstream tasks such as neural network force fields, pursuing representations that are both interpretable and generalizable.
Hover or focus any block to trace how it flows through the framework.
Materials development involves a long decision chain spanning literature retrieval, data integration, multi-scale simulation, and process optimization. My work places intelligent agents at the center, orchestrating knowledge graphs, cross-scale simulation tools, predictive models, and process optimizers into an automated pipeline — with integrated circuit materials as the primary application domain.
Hover or focus a tool or a stage to see how the agent connects to it.
Shanghai Institute of Microsystem and Information Technology, CAS · Shanghai, China
Working with Prof. Wenjie Yu on AI-driven materials research. Awarded the Shanghai Super Postdoc Program.
RIKEN Center for Advanced Intelligence Project (AIP) · Japan
Collaborated with Dr. Qibin Zhao on multimodal representation learning for polymer property prediction.
Shanghai Institute of Microsystem and Information Technology, CAS
Doctoral research on physics-informed ML for materials science. Developed neural network force fields, coarse-grained simulation methods, and multimodal polymer representations.
University of Manchester · Manchester, UK
University of Nottingham Ningbo China · Ningbo, China
npj Computational Materials, vol. 11, Art. no. 153, 2025
A multimodal prediction framework fusing SMILES, molecular graphs, descriptors, and 3D conformations with domain knowledge text, achieving state-of-the-art performance on multiple polymer benchmark tasks.
State-of-the-art on multiple polymer benchmarksDOI ↗
Journal of Chemical Information and Modeling, vol. 65, no. 7, pp. 3203–3214, 2025
A coarse-grained molecular dynamics method integrating Boltzmann inversion structural priors with hierarchical neural network potentials, achieving high structural fidelity while improving computational efficiency by an order of magnitude.
Order-of-magnitude efficiency gain, high structural fidelityDOI ↗
ACS Applied Polymer Materials, vol. 6, no. 7, pp. 3666–3675, 2024
A machine learning framework based on weighted-chained-SMILES for copolymer property prediction, effectively encoding monomer sequence information and composition ratios to improve accuracy for copolymer systems.
Encodes monomer sequence and composition ratioDOI ↗
Nanomaterials, vol. 14, no. 1, Art. no. 127, 2024
A combined simulation and experimental study exploring how polyamine additives affect the silicon removal rate in CMP, revealing adsorption mechanisms and providing guidance for slurry formulation design.
Adsorption mechanisms that guide slurry designDOI ↗I'm always open to discussing research collaborations, academic opportunities, or AI for Science projects. Feel free to reach out — I'll do my best to respond promptly.