Tian Xie
Tian Xie
Microsoft Research
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Cited by
Cited by
Crystal graph convolutional neural networks for an accurate and interpretable prediction of material properties
T Xie, JC Grossman
Physical review letters 120 (14), 145301, 2018
Patterning two-dimensional chalcogenide crystals of Bi2Se3 and In2Se3 and efficient photodetectors
W Zheng, T Xie, Y Zhou, YL Chen, W Jiang, S Zhao, J Wu, Y Jing, Y Wu, ...
Nature Communications 6 (1), 1-8, 2015
Machine learning enabled computational screening of inorganic solid electrolytes for suppression of dendrite formation in lithium metal anodes
Z Ahmad, T Xie, C Maheshwari, JC Grossman, V Viswanathan
ACS central science 4 (8), 996-1006, 2018
Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials
T Xie, A France-Lanord, Y Wang, Y Shao-Horn, JC Grossman
Nature communications 10 (1), 1-9, 2019
Hierarchical visualization of materials space with graph convolutional neural networks
T Xie, JC Grossman
The Journal of chemical physics 149 (17), 174111, 2018
Toward designing highly conductive polymer electrolytes by machine learning assisted coarse-grained molecular dynamics
Y Wang, T Xie, A France-Lanord, A Berkley, JA Johnson, Y Shao-Horn, ...
chemistry of Materials 32 (10), 4144-4151, 2020
Predicting charge density distribution of materials using a local-environment-based graph convolutional network
S Gong, T Xie, T Zhu, S Wang, ER Fadel, Y Li, JC Grossman
Physical Review B 100 (18), 184103, 2019
Effect of chemical variations in the structure of poly (ethylene oxide)-based polymers on lithium transport in concentrated electrolytes
A France-Lanord, Y Wang, T Xie, JA Johnson, Y Shao-Horn, ...
Chemistry of Materials 32 (1), 121-126, 2019
Crystal diffusion variational autoencoder for periodic material generation
T Xie, X Fu, OE Ganea, R Barzilay, T Jaakkola
arXiv preprint arXiv:2110.06197, 2021
Charting lattice thermal conductivity for inorganic crystals and discovering rare earth chalcogenides for thermoelectrics
T Zhu, R He, S Gong, T Xie, P Gorai, K Nielsch, JC Grossman
Energy & Environmental Science 14 (6), 3559-3566, 2021
Surpassing the exciton diffusion limit in single-walled carbon nanotube sensitized solar cells
GI Koleilat, M Vosgueritchian, T Lei, Y Zhou, DW Lin, F Lissel, P Lin, ...
ACS nano 10 (12), 11258-11265, 2016
Chemically engineered substrates for patternable growth of two-dimensional chalcogenide crystals
M Wang, J Wu, L Lin, Y Liu, B Deng, Y Guo, Y Lin, T Xie, W Dang, Y Zhou, ...
ACS nano 10 (11), 10317-10323, 2016
Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties
T Xie, A France-Lanord, Y Wang, J Lopez, MA Stolberg, M Hill, ...
Nature communications 13 (1), 1-10, 2022
Simulate Time-integrated Coarse-grained Molecular Dynamics with Geometric Machine Learning
X Fu, T Xie, NJ Rebello, BD Olsen, T Jaakkola
arXiv preprint arXiv:2204.10348, 2022
Atomistic graph networks for experimental materials property prediction
T Xie, V Bapst, AL Gaunt, A Obika, T Back, D Hassabis, P Kohli, ...
arXiv preprint arXiv:2103.13795, 2021
Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations
X Fu, Z Wu, W Wang, T Xie, S Keten, R Gomez-Bombarelli, T Jaakkola
arXiv preprint arXiv:2210.07237, 2022
Calibrating DFT Formation Enthalpy Calculations by Multifidelity Machine Learning
S Gong, S Wang, T Xie, WH Chae, R Liu, Y Shao-Horn, JC Grossman
JACS Au 2 (9), 1964-1977, 2022
Human-and machine-centred designs of molecules and materials for sustainability and decarbonization
J Peng, D Schwalbe-Koda, K Akkiraju, T Xie, L Giordano, Y Yu, CJ Eom, ...
Nature Reviews Materials, 1-19, 2022
Examining graph neural networks for crystal structures: limitation on capturing periodicity
S Gong, T Xie, Y Shao-Horn, R Gomez-Bombarelli, JC Grossman
arXiv e-prints, arXiv: 2208.05039, 2022
Deep learning methods for the design and understanding of solid materials
T Xie
Massachusetts Institute of Technology, 2020
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