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20172026
most citedCrystal Graph Convolutional Neural Networks for an Accurate and Interpretable Prediction of Material Properties

2.9k citations · 3.8k across the 15 of their papers we have counts for

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Showing 2019Show all

2 papers · 1 filter

physics.comp-ph2019★ 69 cited

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

Sheng Gong, Tian Xie, Taishan Zhu +4

Electron charge density distribution of materials is one of the key quantities in computational materials science as theoretically it determines the ground state energy and practic…

cond-mat.mtrl-sci2019★ 145 cited

Graph Dynamical Networks for Unsupervised Learning of Atomic Scale Dynamics in Materials

Tian Xie, Arthur France-Lanord, Yanming Wang +2

Understanding the dynamical processes that govern the performance of functional materials is essential for the design of next generation materials to tackle global energy and envir…