Deep-learning estimation of band gap with the reading-periodic-table method and periodic convolution layer

In this study, the deep learning method named reading periodic table, which utilizes deep learning to read the periodic table and the laws of the elements, was extended. The method now also learns the periodicity behind the periodic table, that is, the left- and right-most columns are adjacent to one another behind the table with one row shifted at the learning representation level. While the original method handles the table as it is, the extended method treats the periodic table as if its two edges are connected. This is achieved using novel layers named periodic convolution layers, which can handle inputs having periodicity and may be applied to other problems related to computer vision, time series, and so on if the data possesses some periodicity. In the reading periodic table method, no input of any material feature or descriptor is required. We verified that the method is also applicable for estimating the band gap of materials other than superconductors, for which the method was originally applied. We demonstrated two types of deep learning estimation: methods to estimate the existence of a band gap and those to estimate the value of the band gap given that the materials were known to have one. Finally, we discuss the limitations of the dataset and model evaluation method. We may be unable to distinguish good models based on the random train--test split scheme; thus, we must prepare an appropriate dataset where the training and test data are temporally separate.

Deep Learning of Superconductors I: Estimation of Critical Temperature of Superconductors Toward the Search for New Materials

High-temperature superconductors have a lot of promising applications: quantum computers, high-performance classical computers, green energy, safe and high-speed transportation system, medical appliance, and etc. However, to discover new superconductors is very difficult. It is said that only $3\%$ of candidate materials become superconductors. We do not have satisfactory theory of high-temperature superconductors yet, and computational methods do not work either. On the other hand, the data has accumulated. Deep learning suits the situation. We introduce following two methods into deep learning: (1) in order to better represent material information and make good use of our scientific knowledge into deep learning, we introduce the input data form representing "material as image from periodic table with four channels corresponding to s, p, d, and f of electron orbit", (2) we introduce the method, named "garbage-in", to make use of the non-annotated data, which means the data of materials without any critical temperature $T_c$ in this case. We show that the critical temperature is well predicted by deep learning. In order to test the ability of the deep neural network it is good if we have the list of materials, which it is hard to tell whether they become superconductors or not from human expert in advance. We use the list made by Hosono's group as such. The test is to predict if a material becomes superconductor beyond $10$ Kelvin or not, and the neural network is trained by the data before the list was made. The neural network achieved accuracy $94\%$, precision $74\%$, recall $62\%$, and f1 score $67\%$. Compared to the baseline precision $9\%$, which is obtained from a positive prediction to randomly selected material, our deep learning has very good precision. We make a list of candidate materials of superconductors, and we are preparing for the experiments now.

Pseudo-Feature Generation for Imbalanced Data Analysis in Deep Learning

We generate pseudo-features by multivariate probability distributions obtained from feature maps in a low layer of trained deep neural networks. Then, we virtually augment the data of minor classes by the pseudo-features in order to overcome imbalanced data problems. Because all the wild data are imbalanced, the proposed method has the possibility to improve the ability of DNN in a broad range of problems

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. The method is that we train only the final classifier again after an ordinary training is done.