뉴스 & 기능
어워드 | MIT Technology Review
Tian Xie named to MIT Technology Review’s 2025 Innovators Under 35
Tian led the development of MatterGen, our generative AI model for materials discovery.
Ideas: AI for materials discovery with Tian Xie and Ziheng Lu
| Lindsay Kalter, Ziheng Lu, 그리고 Tian Xie
How do you generate and test materials that don’t exist yet? Researchers Tian Xie and Ziheng Lu share the story behind MatterGen and MatterSim, AI tools poised to transform materials discovery and help drive advances in energy, manufacturing, and sustainability.
MatterGen: A new paradigm of materials design with generative AI
| Claudio Zeni, Robert Pinsler, Daniel Zügner, Andrew Fowler, Matthew Horton, Ryota Tomioka, 그리고 Tian Xie
Microsoft researchers introduce MatterGen, a model that can discover new materials tailored to specific needs—like efficient solar cells or CO2 recycling—advancing progress beyond trial-and-error experiments.
MatterSim: A deep-learning model for materials under real-world conditions
| Han Yang, Jielan Li, Hongxia Hao, 그리고 Ziheng Lu
Property prediction for materials under realistic conditions has been a long-standing challenge within the digital transformation of materials design. MatterSim investigates atomic interactions from the very fundamental principles of quantum mechanics.
MatterGen: Property-guided materials design
| Andrew Fowler, Matthew Horton, Ryota Tomioka, Robert Pinsler, Tian Xie, Claudio Zeni, 그리고 Daniel Zügner
The central problem in materials science is to discover materials with desired properties. MatterGen enables broad property-guided materials design.