Identification of advanced spin-driven thermoelectric materials via interpretable machine learning

Yuma Iwasaki,Akihiro Kirihara,Hiroko Someya,Shinichi Yorozu,Masahiko Ishida,Valentin Stanev,Eiji Saitoh,Ichiro Takeuchi,Yasutomo Omori,Ryohto Sawada

doi:10.1038/s41524-019-0241-9

Abstract

Machine learning is becoming a valuable tool for scientific discovery. Particularly attractive is the application of machine learning methods to the field of materials development, which enables innovations by discovering new and better functional materials. To apply machine learning to actual materials development, close collaboration between scientists and machine learning tools is necessary. However, such collaboration has been so far impeded by the black box nature of many machine learning algorithms. It is often difficult for scientists to interpret the data-driven models from the viewpoint of material science and physics. Here, we demonstrate the development of spin-driven thermoelectric materials with anomalous Nernst effect by using an interpretable machine learning method called factorized asymptotic Bayesian inference hierarchical mixture of experts (FAB/HMEs). Based on prior knowledge of material science and physics, we were able to extract from the interpretable machine learning some surprising correlations and new knowledge about spin-driven thermoelectric materials. Guided by this, we carried out an actual material synthesis that led to the identification of a novel spin-driven thermoelectric material. This material shows the largest thermopower to date.

Highlights

Recent progresses of materials science technologies enable the collection of large volumes of materials data in a short time[1,2,3,4]
It is difficult for a human to understand the models constructed by a deep neural network[9], expressed as the connections between large numbers of perceptrons
X14}, whose simple descriptions are shown in Fig. 3d, were obtained by density function theory (DFT) calculation based on composition data experimentally obtained from X-ray fluorescence (XRF) measurement

Summary

Introduction

Recent progresses of materials science technologies enable the collection of large volumes of materials data in a short time[1,2,3,4]. Machine learning technologies are extremely promising, due to their ability to rapidly analyze data[5,6,7,8], and for their potential to discover novel knowledge, not rooted in conventional theories. To apply machine learning for actual materials development, cooperation between scientists and machine learning tools is necessary. Materials scientists often try to understand the rationale behind the data-driven models in order to obtain some actionable information to guide materials development. Such attempts have been impeded so far by the low interpretability of many machine learning methods. The notion of interpretable machine learning (explainable or transparent machine learning), which has high predictive ability and high interpretability, has recently seen a resurgence[10,11], especially in the field of scientific discovery

Methods

Results

Conclusion