T. Xie

发表

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties.

Jeffrey C Grossman, Tian Xie, J. Grossman, 2017, Physical review letters.

Simulate Time-integrated Coarse-grained Molecular Dynamics with Geometric Machine Learning

Nathan J. Rebello, T. Jaakkola, B. Olsen, 2022, ArXiv.

A cloud platform for automating and sharing analysis of raw simulation data from high throughput polymer molecular dynamics simulations

J. Grossman, Y. Shao-horn, A. France-Lanord, 2022, ArXiv.

Human- and machine-centred designs of molecules and materials for sustainability and decarbonization

J. Grossman, Reshma R. Rao, L. Giordano, 2022, Nature Reviews Materials.

Simulate Time-integrated Coarse-grained Molecular Dynamics with Geometric Machine Learning

Nathan J. Rebello, T. Jaakkola, Tian Xie, 2022, ArXiv.

Examining graph neural networks for crystal structures: limitations and opportunities for capturing periodicity

J. Grossman, Y. Shao-horn, Rafael Gómez-Bombarelli, 2022, 2208.05039.

Examining graph neural networks for crystal structures: limitation on capturing periodicity

J. Grossman, Y. Shao-horn, Rafael Gómez-Bombarelli, 2022 .

Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

T. Jaakkola, S. Keten, Wujie Wang, 2022, ArXiv.

Human- and machine-centred designs of molecules and materials for sustainability and decarbonization

J. Grossman, Reshma R. Rao, L. Giordano, 2022, Nature Reviews Materials.

Toward Designing Highly Conductive Polymer Electrolytes by Machine Learning Assisted Coarse-Grained Molecular Dynamics

Jeremiah A. Johnson, J. Grossman, Y. Shao-horn, 2020, Chemistry of Materials.

Accelerating amorphous polymer electrolyte screening by learning to reduce errors in molecular dynamics simulated properties

Jeremiah A. Johnson, J. Grossman, Y. Shao-horn, 2021, Nature Communications.

Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials

Jeffrey C. Grossman, Yang Shao-Horn, Yanming Wang, 2019, Nature Communications.

Hierarchical Visualization of Materials Space with Graph Convolutional Neural Networks

Tian Xie, Jeffrey C. Grossman, J. Grossman, 2018, The Journal of chemical physics.

Charting lattice thermal conductivity for inorganic crystals and discovering rare earth chalcogenides for thermoelectrics

T. Zhu, J. Grossman, K. Nielsch, 2021, Energy & Environmental Science.

Effect of Chemical Variations in the Structure of Poly(ethylene oxide)-Based Polymers on Lithium Transport in Concentrated Electrolytes

Jeffrey C. Grossman, Yang Shao-Horn, Tian Xie, 2019, Chemistry of Materials.

Machine Learning Enabled Computational Screening of Inorganic Solid Electrolytes for Suppression of Dendrite Formation in Lithium Metal Anodes

J. Grossman, Tian Xie, C. Maheshwari, 2018, ACS central science.

Inverse design of next-generation superconductors using data-driven deep generative models

K. Choudhary, T. Xie, D. Wines, 2023, 2304.08446.

Calibrating DFT Formation Enthalpy Calculations by Multifidelity Machine Learning

J. Grossman, Y. Shao-horn, Sheng Gong, 2021, JACS Au.

Examining graph neural networks for crystal structures: Limitations and opportunities for capturing periodicity

J. Grossman, Y. Shao-horn, Rafael Gómez-Bombarelli, 2022, Science advances.

Charting Lattice Thermal Conductivity of Inorganic Crystals

Jeffrey C. Grossman, Tian Xie, Sheng Gong, 2020, 2006.11712.

Predicting charge density distribution of materials using a local-environment-based graph convolutional network

T. Zhu, J. Grossman, Ya-wei Li, 2019, Physical Review B.

M2Hub: Unlocking the Potential of Machine Learning for Materials Discovery

Carla P. Gomes, J. Gregoire, Yanqiao Zhu, 2023, ArXiv.

Graph dynamical networks for unsupervised learning of atomic scale dynamics in materials

J. Grossman, Y. Shao-horn, Yanming Wang, 2019, Nature Communications.

Accelerating the screening of amorphous polymer electrolytes by learning to reduce random and systematic errors in molecular dynamics simulations pdf

Jeffrey C. Grossman, Yang Shao-Horn, Jeremiah A. Johnson, 2021, ArXiv.

Accelerate the screening of complex materials by learning to reduce random and systematic errors

Jeremiah A. Johnson, J. Grossman, Y. Shao-horn, 2020 .

Forces are not Enough: Benchmark and Critical Evaluation for Machine Learning Force Fields with Molecular Simulations

T. Jaakkola, S. Keten, Wujie Wang, 2022, Trans. Mach. Learn. Res..

Simulate Time-integrated Coarse-grained Molecular Dynamics with Multi-Scale Graph Networks

Nathan J. Rebello, T. Jaakkola, B. Olsen, 2022, 2204.10348.

Crystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties.

J. Grossman, T. Xie, 2017, Physical review letters.

A cloud platform for sharing and automated analysis of raw data from high throughput polymer MD simulations

Y. Shao-horn, A. France-Lanord, A. Khajeh, 2023, APL Machine Learning.

Inverse Design of Next-Generation Superconductors Using Data-Driven Deep Generative Models.

K. Choudhary, T. Xie, D. Wines, 2023, The journal of physical chemistry letters.