294 citations · 889 across the 15 of their papers we have counts for
11 papers · 1 filter
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…
Physics Guided Deep Learning for Generative Design of Crystal Materials with Symmetry Constraints
Yong Zhao, Edirisuriya M. Dilanga Siriwardane, Zhenyao Wu +4
Discovering new materials is a challenging task in materials science crucial to the progress of human society. Conventional approaches based on experiments and simulations are labo…
Semi-supervised teacher-student deep neural network for materials discovery
Daniel Gleaves, Edirisuriya M. Dilanga Siriwardane, Yong Zhao +2
Data driven generative machine learning models have recently emerged as one of the most promising approaches for new materials discovery. While the generator models can generate mi…
Physics guided deep learning generative models for crystal materials discovery
Yong Zhao, Edirisuriya MD Siriwardane, Jianjun Hu
Deep learning based generative models such as deepfake have been able to generate amazing images and videos. However, these models may need significant transformation when applied…
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…
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…
AlphaCrystal: Contact map based crystal structure prediction using deep learning
Jianjun Hu, Yong Zhao, Qin Li +4
Crystal structure prediction is one of the major unsolved problems in materials science. Traditionally, this problem is formulated as a global optimization problem for which global…
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…
Lattice Thermal Conductivity Prediction using Symbolic Regression and Machine Learning
Christian Loftis, Kunpeng Yuan, Yong Zhao +2
Prediction models of lattice thermal conductivity have wide applications in the discovery of thermoelectrics, thermal barrier coatings, and thermal management of semiconductors. kL…
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…