Feature Space of XRD Patterns Constructed by an Autoencoder-Reference-Cited by-同舟云学术

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Feature Space of XRD Patterns Constructed by an Autoencoder

Published:2022-12-18 Issue:2 Volume:6 Page:2200613
ISSN:2513-0390
Container-title:Advanced Theory and Simulations
language:en
Short-container-title:Advcd Theory and Sims

Author:

Utimula Keishu¹^ORCID,Yano Masao²,Kimoto Hiroyuki²,Hongo Kenta³^ORCID,Nakano Kousuke⁴^ORCID,Maezono Ryo⁴^ORCID

Affiliation:

1. School of Materials Science JAIST Asahidai 1‐1 Nomi Ishikawa 923‐1292 Japan

2. Toyota Motor Corporation 1, Toyota‐cho Toyota Aichi 471‐8572 Japan

3. Research Center for Advanced Computing Infrastructure JAIST Asahidai 1‐1 Nomi Ishikawa 923‐1292 Japan

4. School of Information Science JAIST Asahidai 1‐1 Nomi Ishikawa 923‐1292 Japan

Funder

Toyota Motor Corporation

Air Force Office of Scientific Research

Publisher

Wiley

Subject

Multidisciplinary,Modeling and Simulation,Numerical Analysis,Statistics and Probability

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/adts.202200613

Reference52 articles.

1. Bayesian molecular design with a chemical language model

2. AFLOW-CHULL: Cloud-Oriented Platform for Autonomous Phase Stability Analysis

3. Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries

4. Machine‐Learning Clustering Technique Applied to Powder X‐Ray Diffraction Patterns to Distinguish Compositions of ThMn 12 ‐Type Alloys

5. On adaptive control processes

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3. Effect of solid content on the piezoelectric properties of porous (Ba0.85Ca0.15)(Zr0.1Ti0.9)O3 ceramics;Ceramics International;2023-11

4. Deep generative learning for exploration in large electrochemical impedance dataset;Engineering Applications of Artificial Intelligence;2023-11

5. X-ray Diffraction Data Analysis by Machine Learning Methods—A Review;Applied Sciences;2023-09-04

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