Tian Xie

Researcher & Project Lead @ Microsoft Research AI for Science.

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I am a principal research manager and project lead at Microsoft Research AI for Science. I lead a highly interdisciplinary team of researchers, engineers, and program managers to develop foundational AI capabilities to accelerate the design of novel materials, aiming to impact broad areas including energy storage, carbon capture, and catalysis. I lead the development of MatterGen, an AI generator that discovers novel materials. Our team also develops MatterSim, an AI emulator that accelerates the simulation of material properties.

Before Microsoft, I was a postdoc in the Computer Science and Artificial Intelligence Laboratory (CSAIL) at MIT from 2020 to 2022, co-advised by Tommi Jaakkola and Regina Barzilay. I got my PhD in Materials Science and Engineering at MIT in 2020, advised by Jeffrey C. Grossman. I also did research internships at DeepMind and Google X.

My most noticeable work before Microsoft includes the development of CDVAE in 2021, a generative model for materials that significantly surpassing other models at the time, as well as CGCNN in 2018, the first graph neural network specifically designed for materials.

recent news [earlier]

Jan 16, 2025 We announced the publication of MatterGen on Nature. It represents a new paradigm of materials design with generative AI. We also released MatterGen code and model checkpoints on GitHub under MIT license. [Blog] [Paper] [Code] [Story] [Podcast]
Jul 30, 2024 I won the Frontier of Science Award of the International Congress of Basic Science, together with my PhD advisor Jeffrey Grossman, for our work CGCNN to AI for Physical Sciences. [Link]
May 13, 2024 We announced MatterSim, an emulator for accurate and efficient materials simulation and property prediction over a broad range of elements, temperatures, and pressures. [Arxiv] [Twitter] [Blog]
Dec 06, 2023 We announced MatterGen, a generative model that enables broad property-guided materials design for inorganic materials. [Arxiv] [Twitter] [Video] [Blog]
Oct 16, 2023 We released MOFDiff, a coarse-grained diffusion model to design MOFs for carbon capture. [Arxiv] [Twitter] [Code]

selected publications [full list]

  1. Nature
    A generative model for inorganic materials design
    Claudio Zeni, Robert Pinsler, Daniel Zügner, Andrew Fowler, Matthew Horton, Xiang Fu, Zilong Wang, Aliaksandra Shysheya, Jonathan Crabbé, Shoko Ueda, Roberto Sordillo, Lixin Sun, Jake Smith, Bichlien Nguyen, Hannes Schulz, Sarah Lewis, Chin-Wei Huang, Ziheng Lu, Yichi Zhou, Han Yang, Hongxia Hao, Jielan Li, Chunlei Yang, Wenjie Li, Ryota Tomioka, and Tian Xie
    Nature, 2025
  2. arXiv
    MatterSim: A Deep Learning Atomistic Model Across Elements, Temperatures and Pressures
    Han Yang, Chenxi Hu, Yichi Zhou, Xixian Liu, Yu Shi, Jielan Li, Guanzhi Li, Zekun Chen, Shuizhou Chen, Claudio Zeni, Matthew Horton, Robert Pinsler, Andrew Fowler, Daniel Zügner, Tian Xie, Jake Smith, Lixin Sun, Qian Wang, Lingyu Kong, Chang Liu, Hongxia Hao, and Ziheng Lu
    arXiv preprint arXiv:2405.04967, 2024
  3. ICLR 2024
    MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework Design
    Xiang Fu, Tian Xie , Andrew Scott Rosen, Tommi S. Jaakkola, and Jake Allen Smith
    In The Twelfth International Conference on Learning Representations, 2024
  4. ICLR 2022
    Crystal Diffusion Variational Autoencoder for Periodic Material Generation
    Tian Xie, Xiang Fu, Octavian-Eugen Ganea, Regina Barzilay, and Tommi Jaakkola
    International Conference on Learning Representations (ICLR), 2021
  5. Phys. Rev. Lett.
    Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties
    Tian Xie, and Jeffrey C Grossman
    Physical review letters, 2018