3 citations · 4 across the 3 of their papers we have counts for
6 papers
Scalable deeper graph neural networks for high-performance materials property prediction
Sadman Sadeed Omee, Steph-Yves Louis, Nihang Fu +5
Machine learning (ML) based materials discovery has emerged as one of the most promising approaches for breakthroughs in materials science. While heuristic knowledge based descript…
MaterialsAtlas.org: A Materials Informatics Web App Platform for Materials Discovery and Survey of State-of-the-Art
Jianjun Hu, Stanislav Stefanov, Yuqi Song +4
The availability and easy access of large scale experimental and computational materials data have enabled the emergence of accelerated development of algorithms and models for mat…
Active learning based generative design for the discovery of wide bandgap materials
Rui Xin, Edirisuriya M. D. Siriwardane, Yuqi Song +4
Active learning has been increasingly applied to screening functional materials from existing materials databases with desired properties. However, the number of known materials de…
Predicting Elastic Properties of Materials from Electronic Charge Density Using 3D Deep Convolutional Neural Networks
Yong Zhao, Kunpeng Yuan, Yinqiao Liu +3
Materials representation plays a key role in machine learning based prediction of materials properties and new materials discovery. Currently both graph and 3D voxel representation…
Global Attention based Graph Convolutional Neural Networks for Improved Materials Property Prediction
Steph-Yves Louis, Yong Zhao, Alireza Nasiri +4
Machine learning (ML) methods have gained increasing popularity in exploring and developing new materials. More specifically, graph neural network (GNN) has been applied in predict…
Machine Learning based prediction of noncentrosymmetric crystal materials
Yuqi Song, Joseph Lindsay, Yong Zhao +5
Noncentrosymmetric materials play a critical role in many important applications such as laser technology, communication systems,quantum computing, cybersecurity, and etc. However,…