🤖 Intelligent Design Platforms for Advanced Materials and Structures

We develop cutting-edge artificial intelligence and machine learning platforms for the efficient design and discovery of advanced materials and structures. Our research integrates deep learning architectures, physics-informed neural networks, and active learning strategies to accelerate materials development and optimization. These platforms enable inverse design capabilities, multi-property prediction, and process-structure-property relationship understanding across various material systems. Through innovative computational frameworks, we bridge the gap between materials science and artificial intelligence to enable rapid materials discovery and design.

Currently, my representative works in this field include：

• End-to-End Generative Deep Learning for Two-Dimensional Material Morphology Prediction on Arbitrary Topographical Substrate, Ruimin Zeng, Jianming Cai, Yan Chen, Yilun Liu.
• MatterGPT 2024 MatterGPT: A Generative Transformer for Multi-Property Inverse Design of Solid-State Materials, Yan Chen, Xueru Wang, Xiaobin Deng, Yilun Liu, Xi Chen, Yunwei Zhang, Lei Wang, Hang Xiao Paper Code
• PSAL 2024 A process-synergistic active learning framework for high-strength Al-Si alloys design, Jianming Cai, Mengxia Han, Xirui Yan, Yan Chen, et al.
• AFM 2024 Review of External Field Effects on Electrocatalysis: Machine Learning Guided Design, Lei Wang, Xuyan Zhou, Zihan Luo, Sida Liu, Shengying Yue, Yan Chen, Yilun Liu.
• NC 2023 An invertible, invariant crystal representation for inverse design of solid-state materials using generative deep learning , Hang Xiao, Rong Li, Xiaoyang Shi, Yan Chen, Liangliang Zhu, Xi Chen, Lei Wang. Paper Code