publications

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

2025

  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

2024

  1. 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
  2. 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

2023

  1. NeurIPS 2023
    M2Hub: Unlocking the Potential of Machine Learning for Materials Discovery
    Yuanqi Du, Yingheng Wang, Yining Huang, Jianan Canal Li, Yanqiao Zhu, Tian Xie, Chenru Duan, John Gregoire, and Carla P Gomes
    Advances in Neural Information Processing Systems, 2023
  2. APL mach. learn.
    A cloud platform for sharing and automated analysis of raw data from high throughput polymer MD simulations
    Tian Xie, Ha-Kyung Kwon, Daniel Schweigert, Sheng Gong, Arthur France-Lanord, Arash Khajeh, Emily Crabb, Michael Puzon, Chris Fajardo, Will Powelson, and  others
    APL Machine Learning, 2023
  3. The impact of large language models on scientific discovery: a preliminary study using gpt-4
    Microsoft Research AI4Science, and Microsoft Azure Quantum
    arXiv preprint arXiv:2311.07361, 2023
  4. Sci. Adv.
    Examining graph neural networks for crystal structures: limitations and opportunities for capturing periodicity
    Sheng Gong, Keqiang Yan, Tian Xie, Yang Shao-Horn, Rafael Gomez-Bombarelli, Shuiwang Ji, and Jeffrey C Grossman
    Science Advances, 2023
  5. J. Phys. Chem. Lett.
    Inverse design of next-generation superconductors using data-driven deep generative models
    Daniel Wines, Tian Xie, and Kamal Choudhary
    The Journal of Physical Chemistry Letters, 2023
  6. TMLR
    Simulate time-integrated coarse-grained molecular dynamics with multi-scale graph networks
    Xiang Fu, Tian Xie, Nathan J Rebello, Bradley Olsen, and Tommi S Jaakkola
    Transactions on Machine Learning Research, 2023
  7. TMLR
    Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations
    Xiang Fu, Zhenghao Wu, Wujie Wang, Tian Xie, Sinan Keten, Rafael Gomez-Bombarelli, and Tommi Jaakkola
    Transactions on Machine Learning Research, 2023

2022

  1. Nat. Rev. Mater.
    Human-and machine-centred designs of molecules and materials for sustainability and decarbonization
    Jiayu Peng, Daniel Schwalbe-Koda, Karthik Akkiraju, Tian Xie, Livia Giordano, Yang Yu, C John Eom, Jaclyn R Lunger, Daniel J Zheng, Reshma R Rao, and  others
    Nature Reviews Materials, 2022
  2. JACS Au
    Calibrating dft formation enthalpy calculations by multifidelity machine learning
    Sheng Gong, Shuo Wang, Tian Xie, Woo Hyun Chae, Runze Liu, Yang Shao-Horn, and Jeffrey C Grossman
    JACS Au, 2022
  3. Nat. Commun.
    Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties
    Tian Xie, Arthur France-Lanord, Yanming Wang, Jeffrey Lopez, Michael A Stolberg, Megan Hill, Graham Michael Leverick, Rafael Gomez-Bombarelli, Jeremiah A Johnson, Yang Shao-Horn, and  others
    Nature communications, 2022

2021

  1. 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
  2. Atomistic graph networks for experimental materials property prediction
    Tian* Xie, Victor* Bapst, Alexander L Gaunt, Annette Obika, Trevor Back, Demis Hassabis, Pushmeet Kohli, and James Kirkpatrick
    arXiv preprint arXiv:2103.13795, 2021
  3. Energy Environ. Sci.
    Charting Lattice Thermal Conductivity for Inorganic Crystals and Discovering Rare Earth Chalcogenides for Thermoelectrics
    Taishan* Zhu, Ran* He, Sheng* Gong, Tian Xie, Prashun Gorai, Kornelius Nielsch, and Jeffrey C Grossman
    Energy & Environmental Science, 2021

2020

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

2019

  1. Chem. Mater.
    Effect of Chemical Variations in the Structure of Poly (ethylene oxide)-Based Polymers on Lithium Transport in Concentrated Electrolytes
    Arthur France-Lanord, Yanming Wang, Tian Xie, Jeremiah A Johnson, Yang Shao-Horn, and Jeffrey C Grossman
    Chemistry of Materials, 2019
  2. Phys. Rev. B
    Predicting charge density distribution of materials using a local-environment-based graph convolutional network
    Sheng Gong, Tian Xie, Taishan Zhu, Shuo Wang, Eric R Fadel, Yawei Li, and Jeffrey C Grossman
    Physical Review B, 2019
  3. Nat. Commun.
    Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials
    Tian Xie, Arthur France-Lanord, Yanming Wang, Yang Shao-Horn, and Jeffrey C Grossman
    Nature communications, 2019

2018

  1. J. Chem. Phys.
    Hierarchical visualization of materials space with graph convolutional neural networks
    Tian Xie, and Jeffrey C Grossman
    The Journal of chemical physics, 2018
  2. ACS Cent. Sci.
    Machine learning enabled computational screening of inorganic solid electrolytes for suppression of dendrite formation in lithium metal anodes
    Zeeshan Ahmad, Tian Xie, Chinmay Maheshwari, Jeffrey C Grossman, and Venkatasubramanian Viswanathan
    ACS central science, 2018
  3. 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

2016

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

2015

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