3 citations · 3 across the 4 of their papers we have counts for
9 papers
DeepXRD, a Deep Learning Model for Predicting of XRD spectrum from Materials Composition
Rongzhi Dong, Yong Zhao, Yuqi Song +6
One of the long-standing problems in materials science is how to predict a material's structure and then its properties given only its composition. Experimental characterization of…
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…
Computational discovery of new 2D materials using deep learning generative models
Yuqi Song, Edirisuriya M. Dilanga Siriwardane, Yong Zhao +1
Two dimensional (2D) materials have emerged as promising functional materials with many applications such as semiconductors and photovoltaics because of their unique optoelectronic…
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…