Search Results for author:
Found 5 papers, 3 papers with code
Materials Transformers Language Models for Generative Materials Design: a benchmark study
We also find that the properties of the generated samples can be tailored by training the models with selected training sets such as high-bandgap materials.
Physics Guided Deep Learning for Generative Design of Crystal Materials with Symmetry Constraints
Discovering new materials is a challenging task in materials science crucial to the progress of human society.
Semi-supervised teacher-student deep neural network for materials discovery
For synthesizability prediction, our model significantly increases the baseline PU learning's true positive rate from 87. 9\% to 97. 9\% using 1/49 model parameters.
Crystal structure prediction of materials with high symmetry using differential evolution
Our experimental results show that our proposed algorithm CMCrystalHS can effectively solve the problem of inconsistent contact map dimensions and predict the crystal structures with high symmetry.
Computational discovery of new 2D materials using deep learning generative models
Two dimensional (2D) materials have emerged as promising functional materials with many applications such as semiconductors and photovoltaics because of their unique optoelectronic properties.
