activity
20202022
most citedGlobal Attention based Graph Convolutional Neural Networks for Improved Materials Property Prediction

279 citations · 495 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV2022★ 7 cited

Unsupervised Object Representation Learning using Translation and Rotation Group Equivariant VAE

Alireza Nasiri, Tristan Bepler

In many imaging modalities, objects of interest can occur in a variety of locations and poses (i.e. are subject to translations and rotations in 2d or 3d), but the location and pos…

cs.SD2021★ 16 cited

SoundCLR: Contrastive Learning of Representations For Improved Environmental Sound Classification

Alireza Nasiri, Jianjun Hu

Environmental Sound Classification (ESC) is a challenging field of research in non-speech audio processing. Most of current research in ESC focuses on designing deep models with sp…

cond-mat.mtrl-sci2021★ 3 cited

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…

cs.LG2021★ 13 cited

NODE-SELECT: A Graph Neural Network Based On A Selective Propagation Technique

Steph-Yves Louis, Alireza Nasiri, Fatima J. Rolland +2

While there exists a wide variety of graph neural networks (GNN) for node classification, only a minority of them adopt mechanisms that effectively target noise propagation during…

cond-mat.mtrl-sci2021★ 146 cited

High-throughput discovery of novel cubic crystal materials using deep generative neural networks

Yong Zhao, Mohammed Al-Fahdi, Ming Hu +4

High-throughput screening has become one of the major strategies for the discovery of novel functional materials. However, its effectiveness is severely limited by the lack of quan…

physics.comp-ph2020★ 279 cited

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…

physics.comp-ph2020★ 31 cited

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,…