News & features
MatterSim: A deep-learning model for materials under real-world conditions
| Han Yang, Jielan Li, Hongxia Hao, and 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.
Distributional Graphormer: Toward equilibrium distribution prediction for molecular systems
| Shuxin Zheng, Chang Liu, Yu Shi, Ziheng Lu, Fusong Ju, Jianwei Zhu, Hongxia Hao, Peiran Jin, Frank Noé, Haiguang Liu, and Tie-Yan Liu
Distributional Graphormer, Microsoft’s new deep learning framework for predicting the equilibrium distribution of molecular structures, can generate realistic and diverse molecular structures with high efficiency and low cost.
Introducing the Microsoft Climate Research Initiative
Addressing and mitigating the effects of climate change requires a collective effort, bringing our strengths to bear across industry, government, academia, and civil society.