NDLI: Quantum Energy Prediction Using Graph Kernel

Content Provider	IEEE Xplore Digital Library
Author	Duan, J. Seko, A. Kashima, H.
Copyright Year	2015
Description	Author affiliation: Dept. of Mater. Sci. & Eng., Kyoto Univ. Kyoto, Kyoto, Japan (Seko, A.) \|\| Dept. of Intell. Sci. & Technol., Kyoto Univ., Kyoto, Japan (Duan, J.; Kashima, H.)
Abstract	Vectorial compound representation has played an important role in the recent progress in material property prediction based on machine learning methods. However, the material compounds are originally recorded in the material databases as non-vectorial graph units and space groups. The representation of compounds as handmade vectorial representations is challenging and crucial for the successful application of machine learning. In this study, we attempt to use the random walk graph kernel for material property prediction in which the kernel between graph objects can be automatically constructed from the non-vectorial graph representations in the material informatics databases. By constructing the graph representation efficiently from the raw geometric coordinate data using maximum spanning tree, our method achieves approximately the same prediction power as the conventional vectorial compound representation, and even outperforms it in situations where the number of labeled data is limited. This is well suited for the current material informatics practice where numerous potential material structures have not yet been discovered or annotated. Moreover, we demonstrate that our method maintains a flexible framework that allows the inclusion of domain knowledge, such as electromagnetism, by material scientists.
Starting Page	1651
Ending Page	1656
File Size	506199
Page Count	6
File Format	PDF
e-ISBN	9781479986972
DOI	10.1109/SMC.2015.291
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-10-09
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Kernel Compounds Databases Informatics Material properties Symmetric matrices Chemicals
Content Type	Text
Resource Type	Article

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