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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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physics.comp-ph2022★ 170 cited

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

Xiang Fu, Zhenghao Wu, Wujie Wang +4

Molecular dynamics (MD) simulation techniques are widely used for various natural science applications. Increasingly, machine learning (ML) force field (FF) models begin to replace…

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…