An up-to-date list is available on Google Scholar

journal and conference papers


  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)
    International Conference on Learning Representations (ICLR) 2021
    Contributed talk (top 2 %) at the NeurIPS 2021 ML4PS Workshop
  2. Atomistic graph networks for experimental materials property prediction
    Xie, Tian*, Bapst, Victor*, Gaunt, Alexander L, Obika, Annette, Back, Trevor, Hassabis, Demis, Kohli, Pushmeet, and Kirkpatrick, James (*: equal contribution)
    arXiv preprint arXiv:2103.13795 2021
  3. Charting Lattice Thermal Conductivity for Inorganic Crystals and Discovering Rare Earth Chalcogenides for Thermoelectrics
    Zhu, Taishan*, He, Ran*, Gong, Sheng*, Xie, Tian, Gorai, Prashun, Nielsch, Kornelius, and Grossman, Jeffrey C (*: equal contribution)
    Energy & Environmental Science 2021
  4. 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.
    arXiv preprint arXiv:2101.05339 2021
    Spotlight Talk at NeurIPS 2020 ML4Molecules workshop


  1. Toward Designing Highly Conductive Polymer Electrolytes by Machine Learning Assisted Coarse-Grained Molecular Dynamics
    Wang, Yanming*, Xie, Tian*, France-Lanord, Arthur, Berkley, Arthur, Johnson, Jeremiah A, Shao-Horn, Yang, and Grossman, Jeffrey C (*: equal contribution)
    Chemistry of Materials 2020


  1. Effect of Chemical Variations in the Structure of Poly (ethylene oxide)-Based Polymers on Lithium Transport in Concentrated Electrolytes
    France-Lanord, Arthur, Wang, Yanming, Xie, Tian, Johnson, Jeremiah A, Shao-Horn, Yang, and Grossman, Jeffrey C
    Chemistry of Materials 2019
  2. Predicting charge density distribution of materials using a local-environment-based graph convolutional network
    Gong, Sheng, Xie, Tian, Zhu, Taishan, Wang, Shuo, Fadel, Eric R, Li, Yawei, and Grossman, Jeffrey C
    Physical Review B 2019
  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


  1. Hierarchical visualization of materials space with graph convolutional neural networks
    Xie, Tian, and Grossman, Jeffrey C
    The Journal of chemical physics 2018
  2. 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
  3. Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties
    Xie, Tian, and Grossman, Jeffrey C
    Physical review letters 2018


  1. Surpassing the exciton diffusion limit in single-walled carbon nanotube sensitized solar cells
    Koleilat, Ghada I, Vosgueritchian, Michael, Lei, Ting, Zhou, Yan, Lin, Debora W, Lissel, Franziska, Lin, Pei, To, John WF, Xie, Tian, England, Kemar, and others,
    ACS nano 2016
  2. Chemically engineered substrates for patternable growth of two-dimensional chalcogenide crystals
    Wang, Mingzhan, Wu, Jinxiong, Lin, Li, Liu, Yujing, Deng, Bing, Guo, Yunfan, Lin, Yuanwei, Xie, Tian, Dang, Wenhui, Zhou, Yubing, and others,
    ACS nano 2016


  1. Patterning two-dimensional chalcogenide crystals of Bi 2 Se 3 and In 2 Se 3 and efficient photodetectors
    Zheng, Wenshan*, Xie, Tian*, Zhou, Yu*, Chen, YL, Jiang, Wei, Zhao, Shuli, Wu, Jinxiong, Jing, Yumei, Wu, Yue, Chen, Guanchu, and others, (*: equal contribution)
    Nature communications 2015


  1. MIT Theses
    Deep Learning Methods for the Design and Understanding of Solid Materials
    Xie, Tian
    Doctoral Theses at Massachusetts Institute of Technology 2020