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Materials development by interpretable machine learning | | Yuma Iwasaki
; Ryoto Sawada
; Valentin Stanev
; Masahiko Ishida
; Akihiro Kirihara
; Yasutomo Omori
; Hiroko Someya
; Ichiro Takeuchi
; Eiji Saitoh
; Yorozu Shinichi
; | | Date: |
6 Mar 2019 | | Abstract: | Machine learning technologies are expected to be great tools for scientific
discoveries. In particular, materials development (which has brought a lot of
innovation by finding new and better functional materials) is one of the most
attractive scientific fields. To apply machine learning to actual materials
development, collaboration between scientists and machine learning is becoming
inevitable. However, such collaboration has been restricted so far due to black
box machine learning, in which it is difficult for scientists to interpret the
data-driven model from the viewpoint of material science and physics. Here, we
show a material development success story that was achieved by good
collaboration between scientists and one type of interpretable (explainable)
machine learning called factorized asymptotic Bayesian inference hierarchical
mixture of experts (FAB/HMEs). Based on material science and physics, we
interpreted the data-driven model constructed by the FAB/HMEs, so that we
discovered surprising correlation and knowledge about thermoelectric material.
Guided by this, we carried out actual material synthesis that led to
identification of a novel spin-driven thermoelectric material with the largest
thermopower to date. | | Source: | arXiv, 1903.2175 | | Services: | Forum | Review | PDF | Favorites | |
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