Tian Xie

Postdoc @ MIT CSAIL. Previously, PhD @ MIT DMSE, intern @ DeepMind & Google X

Find more about me in these platforms.

Welcome! I am a postdoc in Artificial Intelligence Laboratory (CSAIL) at MIT. I am co-advised by Tommi Jaakkola and Regina Barzilay.

Research interests: I am broadly interested designing physics motivated machine learning algorithms for solving real-world problems related to material design, drug design, and other engineering domains. I study how domain-specific inductive biases can be incorporated into neural networks to improve generalizability and interpretability. I work closely with chemists, material scientists, and other domain experts to deploy machine learning algorithms to accelerate existing workflows and gain scientific insights.

Previously: I got my PhD in Materials Science and Engineering at MIT in 2020, advised by Jeffrey C. Grossman. I got my BS in Chemistry at Peking University in 2015. I was a research intern at DeepMind, working with Pushmeet Kohli and James Kirkpatrick in 2019/05. I was a research intern at Google X in 2019/02.

recent news [earlier]

2021/12 I will give a contributed talk (top 2 %) about our latest CDVAE paper at the NeurIPS ML4Physics workshop on Dec. 13, 2021.
2021/10 Excited to share our latest preprint “Crystal Diffusion Variational Autoencoder for Periodic Material Generation” on arXiv. Update (2021/12): code and data are now available on our github repo.
2021/05 Our lastest work “Charting lattice thermal conductivity for inorganic crystals and discovering rare earth chalcogenides for thermoelectrics is published on Energy & Environmental Science.

selected publications [full list]

  1. Crystal Diffusion Variational Autoencoder for Periodic Material Generation
    Xie, Tian*†, Fu, Xiang*, Ganea, Octavian-Eugen*, Barzilay, Regina, and Jaakkola, Tommi (*: equal contribution, †: corresponding author)
    arXiv preprint arXiv:2110.06197 2021
  2. Accelerating the screening of amorphous polymer electrolytes by learning to reduce random and systematic errors in molecular dynamics simulations
    Xie, Tian, France-Lanord, Arthur, Wang, Yanming, Lopez, Jeffrey, Stolberg, Michael Austin, Hill, Megan, Leverick, Graham Michael, Gomez-Bombarelli, Rafael, Johnson, Jeremiah A., Shao-Horn, Yang, and Grossman, Jeffrey C.
    NeurIPS ML4Molecules workshop 2020
    Spotlight Talk, full paper at arXiv
  3. Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials
    Xie, Tian, France-Lanord, Arthur, Wang, Yanming, Shao-Horn, Yang, and Grossman, Jeffrey C
    Nature communications 2019
  4. Machine learning enabled computational screening of inorganic solid electrolytes for suppression of dendrite formation in lithium metal anodes
    Ahmad, Zeeshan, Xie, Tian, Maheshwari, Chinmay, Grossman, Jeffrey C, and Viswanathan, Venkatasubramanian
    ACS central science 2018
  5. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties
    Xie, Tian, and Grossman, Jeffrey C
    Physical review letters 2018