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Machine Learning in Materials Science
TopicLeading institutions, researchers & key papers
This cluster of papers focuses on the application of materials informatics, machine learning, and high-throughput computational techniques to accelerate materials innovation. It encompasses topics such as property predictions, crystal structures, molecular dynamics, and data mining in the context of materials science and engineering.
27
Works
IDs:OpenAlex
How has Machine Learning in Materials Science's publication output changed over time?
ScholarIQpublication output · 2020–2026
Output grew100% over the shown period — from 1 works in 2020 to 2 in 2026.
1
1
1
1
2
2
202020222023202420252026
What are the most-cited papers on Machine Learning in Materials Science?
ScholarIQmost cited works
Inverse Design of Materials by Machine Learning
Wang Jia, Yingxue Wang, Yanan Chen
Materials. 2022119 CitationsOPEN ACCESS
Molecular generation targeting desired electronic properties <i>via</i> deep generative models
Qi Yuan, Alejandro Santana‐Bonilla, Martijn A. Zwijnenburg, Kim E. Jelfs
S23181512. 202047 CitationsOPEN ACCESS
Artificial intelligence and machine learning-driven design of self-healing biomedical composites
Senthil Maharaj Kennedy, K. Amudhan, K. Padmapriya, R. Robert
Expert Review of Medical Devices. 202517 Citations
A domain knowledge enhanced machine learning method to predict the properties of halide double perovskite A <sub>2</sub> B <sup>+</sup> B <sup>3+</sup> X <sub>6</sub>
Xiao Wei, Yunong Zhang, Xi Liu, Junjie Peng, Shengzhou Li, Renchao Che, Huiran Zhang
S2764437742. 202314 Citations
Accelerated Structural Optimization for the Supported Metal System Based on Hybrid Approach Combining Bayesian Optimization with Local Search
Shinyoung Bae, Dongjae Shin, Haechang Kim, Jeong Woo Han, Jong Min Lee
S189701308. 20243 Citations
Where is Machine Learning in Materials Science research published, and who funds it?
ScholarIQvenues & funding sources
TOP JOURNALS
TOP FUNDERS
National Science Foundation—
NIH—
Wellcome Trust—
European Research Council—
Funder breakdown is a member featureSign up free to unlock
How much of the research on Machine Learning in Materials Science is open access?
ScholarIQopen access share
63%OPEN ACCESS
Gold
13%
Green
25%
Hybrid
25%
Bronze
0%
Closed
38%
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