Predicting Materials Properties with Little Data Using Shotgun Transfer Learning-Reference-Cited by-同舟云学术

Predicting Materials Properties with Little Data Using Shotgun Transfer Learning

Published:2019-09-30 Issue:10 Volume:5 Page:1717-1730
ISSN:2374-7943
Container-title:ACS Central Science
language:en
Short-container-title:ACS Cent. Sci.

Author:

Yamada Hironao¹,Liu Chang¹²,Wu Stephen¹³^ORCID,Koyama Yukinori²^ORCID,Ju Shenghong⁴^ORCID,Shiomi Junichiro²⁴^ORCID,Morikawa Junko²⁵^ORCID,Yoshida Ryo¹²³

Affiliation:

1. The Institute of Statistical Mathematics, Research Organization of Information and Systems, Tachikawa, Tokyo 190-8562, Japan

2. National Institute for Materials Science, Tsukuba, Ibaraki 305-0047, Japan

3. The Graduate University for Advanced Studies, Tachikawa, Tokyo 190-8562, Japan

4. The University of Tokyo, Bunkyo-ku, Tokyo 113-8656, Japan

5. Tokyo Institute of Technology, Meguro-ku, Tokyo 152-8550, Japan

Funder

Japan Society for the Promotion of Science

Japan Science and Technology Agency

Publisher

American Chemical Society (ACS)

Subject

General Chemical Engineering,General Chemistry

Link

http://pubs.acs.org/doi/pdf/10.1021/acscentsci.9b00804

Reference59 articles.

1. Mastering the game of Go without human knowledge

2. Planning chemical syntheses with deep neural networks and symbolic AI

3. Perspective: Materials informatics and big data: Realization of the “fourth paradigm” of science in materials science

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