2023
DOI: 10.1039/d3mh00039g
|View full text |Cite
|
Sign up to set email alerts
|

Machine learning-based inverse design methods considering data characteristics and design space size in materials design and manufacturing: a review

Abstract: In the last few decades, the influence of machine learning has permeated many areas of science and technology, including the field of material science. This toolkit of statistical methods accelerated...

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
1
1

Citation Types

0
2
0

Year Published

2024
2024
2024
2024

Publication Types

Select...
6
1

Relationship

1
6

Authors

Journals

citations
Cited by 22 publications
(2 citation statements)
references
References 139 publications
0
2
0
Order By: Relevance
“…Machine learning (ML) has recently become widely recognized and influential across various scientific disciplines, including the field of materials science, [8][9][10][11][12]. These approaches focused on data have greatly expedited the process of exploring and developing innovative materials.…”
Section: The Fusion Of Materials Science Tetrahedron Paradigm and Dee...mentioning
confidence: 99%
“…Machine learning (ML) has recently become widely recognized and influential across various scientific disciplines, including the field of materials science, [8][9][10][11][12]. These approaches focused on data have greatly expedited the process of exploring and developing innovative materials.…”
Section: The Fusion Of Materials Science Tetrahedron Paradigm and Dee...mentioning
confidence: 99%
“…Further evidence of the importance of inverse problems lies in the growing interest in combining them with design strategies [7][8][9][10][11]. For example, imagine that we wish to build a hypothetical physical system that responds to external stimuli in a certain way.…”
Section: Introductionmentioning
confidence: 99%