Search Results for author:
Found 4 papers, 2 papers with code
Deep-Learning Estimation of Band Gap with the Reading-Periodic-Table Method and Periodic Convolution Layer
Deep Learning Model for Finding New Superconductors, which utilizes deep learning to read the periodic table and the laws of the elements, is applicable not only for superconductors, for which the method was originally applied but also for other problems of materials by demonstrating band gap estimations.
Deep Learning Model for Finding New Superconductors
The deep learning can predict superconductivity for a material with a precision of 62%, which shows the usefulness of the model; it found the recently discovered superconductor CaBi2 and another one Hf0. 5Nb0. 2V2Zr0. 3, neither of which is in the superconductor database; and it found Fe-based high-temperature superconductors (discovered in 2008) from the training data before 2008.
Icing on the Cake: An Easy and Quick Post-Learnig Method You Can Try After Deep Learning
We found an easy and quick post-learning method named "Icing on the Cake" to enhance a classification performance in deep learning.
Cavity Filling: Pseudo-Feature Generation for Multi-Class Imbalanced Data Problems in Deep Learning
Herein, we generate pseudo-features based on the multivariate probability distributions obtained from the feature maps in layers of trained deep neural networks.
