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Tomohiko Konno

Publications and source records attributed to Tomohiko Konno.

5 recordsLinked to original sources
arXiv2021

Deep-Learning Estimation of Band Gap with the Reading-Periodic-Table Method and Periodic Convolution Layer

We verified that the deep learning method named reading periodic table introduced by ref. 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. We then extended the method to learn the laws better by directly learning the cylindrical periodicity between the right- and left-most columns in the periodic table at the learning representation level, that is, by considering the left- and right-most columns to be adjacent to each other. Thus, while the original method handles the table as 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 exhibiting periodicity and may be applied to other problems related to computer vision, time series, and so on for data that possess some periodicity. In the reading periodic table method, no material feature or descriptor is required as input. We demonstrated two types of deep learning estimation: methods to estimate the existence of a bandgap, and methods to estimate the value of the bandgap given when the existence of the bandgap in the materials is known. 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. The code and data are open.

cs.LG↗
arXiv2021

Deep Learning Model for Finding New Superconductors

Exploration of new superconductors still relies on the experience and intuition of experts and is largely a process of experimental trial and error. In one study, only 3% of the candidate materials showed superconductivity. Here, we report the first deep learning model for finding new superconductors. We introduced the method named "reading periodic table" which represented the periodic table in a way that allows deep learning to learn to read the periodic table and to learn the law of elements for the purpose of discovering novel superconductors that are outside the training data. It is recognized that it is difficult for deep learning to predict something outside the training data. Although we used only the chemical composition of materials as information, we obtained an $R^{2}$ value of 0.92 for predicting $T_\text{c}$ for materials in a database of superconductors. We also introduced the method named "garbage-in" to create synthetic data of non-superconductors that do not exist. Non-superconductors are not reported, but the data must be required for deep learning to distinguish between superconductors and non-superconductors. We obtained three remarkable results. 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. These results open the way for the discovery of new high-temperature superconductor families. The candidate materials list, data, and method are openly available from the link https://github.com/tomo835g/Deep-Learning-to-find-Superconductors.

cs.LG↗
arXiv2019

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. Further, we augment the minor-class data based on these generated pseudo-features to overcome the imbalanced data problems. The proposed method, i.e., cavity filling, improves the deep learning capabilities in several problems because all the real-world data are observed to be imbalanced.

cs.LG↗
arXiv2017

A Condition for Cooperation in a Game on Complex Networks

We study a condition of favoring cooperation in a Prisoner's Dilemma game on complex networks. There are two kinds of players: cooperators and defectors. Cooperators pay a benefit b to their neighbors at a cost c, whereas defectors only receive a benefit. The game is a death-birth process with weak selection. Although it has been widely thought that b/c> is a condition of favoring cooperation, we find that b/c> is the condition. We also show that among three representative networks, namely, regular, random, and scale-free, a regular network favors cooperation the most, whereas a scale-free network favors cooperation the least. In an ideal scale-free network where network size is infinite, cooperation is never realized. Whether or not the scale-free network and network heterogeneity favor cooperation depends on the details of a game, although it is occasionally believed that scale-free networks favor cooperation irrespective of game structures. If the number of players are small, then the cooperation is favored in scale-free networks.

physics.soc-ph↗