Generative Models for Extrapolation Prediction in Materials Informatics-Reference-Cited by-同舟云学术

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Generative Models for Extrapolation Prediction in Materials Informatics

Published:2021-05-25 Issue:22 Volume:6 Page:14566-14574
ISSN:2470-1343
Container-title:ACS Omega
language:en
Short-container-title:ACS Omega

Author:

Hatakeyama-Sato Kan¹^ORCID,Oyaizu Kenichi¹^ORCID

Affiliation:

1. Department of Applied Chemistry, Waseda University, Tokyo 169-8555, Japan

Funder

Japan Society for the Promotion of Science

Center for Data Science, Waseda University

Research Institute for Science and Engineering, Waseda University

Information Services International-Dentsu, Ltd.

Publisher

American Chemical Society (ACS)

Subject

General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acsomega.1c01716

Reference38 articles.

1. Machine learning in materials informatics: recent applications and prospects

2. Inverse molecular design using machine learning: Generative models for matter engineering

3. New frontiers for the materials genome initiative

4. Machine-enabled inverse design of inorganic solid materials: promises and challenges

5. Integrating multiple materials science projects in a single neural network

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2. Designing unique and high-performance Al alloys via machine learning: Mitigating data bias through active learning;Computational Materials Science;2024-09

3. Inverse design of high entropy alloys using a deep interpretable scheme for materials attribution analysis;Journal of Alloys and Compounds;2024-03

4. Extrapolative prediction of small-data molecular property using quantum mechanics-assisted machine learning;npj Computational Materials;2024-01-10

5. A Machine-Learning-Aided Data Recovery Approach for Predicting Multi-Material Thermal Behaviors in Advanced Test Reactor Capsules;2024

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