Materials inverse design
Start with a target property. Search across composition and crystal structure to discover materials with physics-informed machine learning.
Explore inverse design
Computational Materials Science Group
Soochow University · School of Energy · SIEMIS
Physical discovery. Interpretable AI.
Connect atomic structure to macroscopic behavior. Explore the physics behind superhard compounds and unusual thermal expansion.
Explore physical discoveryMaterials synthesizability & synthesis planning
From whether a material can be synthesized to how. Combine physical similarity, experimental evidence, and language models to assess candidate routes, select precursors, and plan processing conditions.
Explore synthesizability
Recent publications.
View all publicationsThe Shear-Bond Descriptor and Universal Machine-Learning Potential Enable Large-Scale Discovery of Negative Thermal Expansion Materials
Guangming Chen, Shun Tian, Kaiwei Feng, Yilun Liu, Wanjian Yin, Qilong Gao, Ke Zhou
Machine-Learning Potential Molecular Dynamics Reveals the Critical Role of Flexibility in Solid-Liquid Nanofluidic Friction
Junyu Lian, Shiping Jiao, Wan-Jian Yin, Ke Zhou
Elemental Augmentation of Machine Learning Interatomic Potentials
Haibo Xue, Guanjian Cheng, Wan-Jian Yin
People behind the discoveries.
Meet the team
Professor · PI
Wan-Jian Yin
First-principles simulations, energy materials, and full-space inverse materials design.
View profile wjyin@suda.edu.cn
Associate Professor
Ke Zhou
Nanoscale transport physics and AI-driven materials discovery.
View profile zhouke@suda.edu.cnOpen tools. Shared discoveries.
Software and reproducible workflows for the materials research community.
Explore code & platformsFrom the group.
View all newsExplore the next question with us.
We welcome students, postdoctoral researchers, and collaborators across physics, chemistry, materials science, mechanics, and computing.
Contact the group