Neural networks for a quick access to a digital twin of scanning physical property measurements-Reference-Cited by-同舟云学术

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Neural networks for a quick access to a digital twin of scanning physical property measurements

Published:2023 Issue:2 Volume:2 Page:339-345
ISSN:2635-098X
Container-title:Digital Discovery
language:en
Short-container-title:Digital Discovery

Author:

Terashima Kensei¹^ORCID,Baptista de Castro Pedro¹²^ORCID,Esparza Echevarria Miren Garbiñe¹²,Matsumoto Ryo¹,Yamamoto Takafumi D.¹,Saito Akiko T.¹^ORCID,Takeya Hiroyuki¹,Takano Yoshihiko¹²

Affiliation:

1. National Institute for Materials Science, 1-2-1 Sengen, Tsukuba, Japan

2. University of Tsukuba, 1-1-1 Tennoidai, Tsukuba, Japan

Abstract

A simple and fast construction of neural networks is shown to be useful for augmenting physical-property experimental data.

Funder

Japan Science and Technology Agency

Japan Society for the Promotion of Science

Publisher

Royal Society of Chemistry (RSC)

Link

http://pubs.rsc.org/en/content/articlepdf/2023/DD/D2DD00124A

Reference40 articles.

1. Artificial intelligence for search and discovery of quantum materials

2. Commentary: The Materials Project: A materials genome approach to accelerating materials innovation

3. The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies

4. AFLOW: An automatic framework for high-throughput materials discovery

5. Machine-learning-guided discovery of the gigantic magnetocaloric effect in HoB2 near the hydrogen liquefaction temperature

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