279 citations · 712 across the 43 of their papers we have counts for
14 papers · 1 filter
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…
TCSP: a Template based crystal structure prediction algorithm and web server for materials discovery
Lai Wei, Nihang Fu, Edirisuriya M. D. Siriwardane +5
Fast and accurate crystal structure prediction (CSP) algorithms and web servers are highly desirable for exploring and discovering new materials out of the infinite design space. H…
Predicting Lattice Phonon Vibrational Frequencies Using Deep Graph Neural Networks
Nghia Nguyen, Steph-Yves Louis, Lai Wei +3
Lattice vibration frequencies are related to many important materials properties such as thermal and electrical conductivity as well as superconductivity. However, computational ca…
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…
Big Data driven Product Design: A Survey
Huafeng Quan, Shaobo Li, Changchang Zeng +2
With the improvement of living standards, user requirements of modern products are becoming increasingly more diversified and personalized. Traditional product design methods can n…
Crystal structure prediction using age-fitness multi-objective genetic algorithm and coordination number constraints
Wenhui Yang, Edirisuriya M. Dilanga Siriwardane, Jianjun Hu
Crystal structure prediction (CSP) has emerged as one of the most important approaches for discovering new materials. CSP algorithms based on evolutionary algorithms and particle s…
Composition based crystal materials symmetry prediction using machine learning with enhanced descriptors
Yuxin Li, Rongzhi Dong, Wenhui Yang +1
Geometric information such as the space groups and crystal systems plays an important role in the properties of crystal materials. Prediction of crystal system and space group thus…
Crystal structure prediction of materials with high symmetry using differential evolution
Wenhui Yang, Edirisuriya M. Dilanga Siriwardane, Rongzhi Dong +2
Crystal structure determines properties of materials. With the crystal structure of a chemical substance, many physical and chemical properties can be predicted by first-principles…
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…
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…
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…