5 citations · 10 across the 6 of their papers we have counts for
6 papers
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…
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…
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…
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…