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Description:

This review systematically analyzes over 200 publications to explore the growing role of data-driven methods and their potential benefits in accelerating alloy development. The review presents a comprehensive overview of different aspects of alloy innovation by machine learning and other computational approaches used in recent years.
These methods harness the power of advanced simulation techniques and data analytics to expedite materials’ discovery, predict properties, and optimize performance.
Through analysis, significant trends and disparities within the data discerned, while highlighting previously overlooked research gaps, thus underscoring areas that require further exploration.

Rights:

• Creative Commons BY-NC Attribution-NonCommercial 4.0 International
  

Keyword: Alloy development, machine learning, data-driven research, materials informatics, Materials Genome Initiative, Materials databases

Date published: 2024-12-31

Publisher: Informa UK Limited

Journal:

• Science and Technology of Advanced Materials: Methods (ISSN: 27660400) vol. 4 issue. 1 2326305

Funding:

• D3090

Manuscript type: Publisher's version (Version of record)

MDR DOI:

First published URL: https://doi.org/10.1080/27660400.2024.2326305

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Updated at: 2024-06-25 12:30:16 +0900

Published on MDR: 2024-06-25 12:30:16 +0900