279 citations · 514 across the 15 of their papers we have counts for
15 papers
CSPBench: a benchmark and critical evaluation of Crystal Structure Prediction
Lai Wei, Sadman Sadeed Omee, Rongzhi Dong +6
Crystal structure prediction (CSP) is now increasingly used in discovering novel materials with applications in diverse industries. However, despite decades of developments and sig…
AlphaCrystal-II: Distance matrix based crystal structure prediction using deep learning
Yuqi Song, Rongzhi Dong, Lai Wei +2
Computational prediction of stable crystal structures has a profound impact on the large-scale discovery of novel functional materials. However, predicting the crystal structure so…
Discovery of 2D materials using Transformer Network based Generative Design
Rongzhi Dong, Yuqi Song, Edirisuriya M. D. Siriwardane +1
Two-dimensional (2D) materials have wide applications in superconductors, quantum, and topological materials. However, their rational design is not well established, and currently…
Probabilistic Generative Transformer Language models for Generative Design of Molecules
Lai Wei, Nihang Fu, Yuqi Song +2
Self-supervised neural language models have recently found wide applications in generative design of organic molecules and protein sequences as well as representation learning for…
Materials Transformers Language Models for Generative Materials Design: a benchmark study
Nihang Fu, Lai Wei, Yuqi Song +6
Pre-trained transformer language models on large unlabeled corpus have produced state-of-the-art results in natural language processing, organic molecule design, and protein sequen…
Crystal Transformer: Self-learning neural language model for Generative and Tinkering Design of Materials
Lai Wei, Qinyang Li, Yuqi Song +4
Self-supervised neural language models have recently achieved unprecedented success, from natural language processing to learning the languages of biological sequences and organic…
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…
Piezoelectric modulus prediction using machine learning and graph neural networks
Jeffrey Hu, Yuqi Song
Piezoelectric materials are widely used in all kinds of industries such as electric cigarette lighters, diesel engines and x-ray shutters. However, discovering high-performance and…
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…
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,…