Materials Data Mining
Materials Data Mining is a computational methodology within materials informatics that systematically extracts, analyzes, and interprets patterns, relationships, and knowledge from large-scale materials datasets to accelerate the discovery and design of novel materials.
550
Publications
61.6K
Citations
2.2K
Authors
698
Institutions
Publications per year
2017–2026
401
Parents
550
Machine-learning-assisted materials discovery using failed experiments
Paul Raccuglia, Katherine C. Elbert, Philip Adler et al. · Nature · 2016 · 1.7K citations
Natural Sciences, Materials Engineering, Materials Science +12
1–5 of 550
2.2K
Lawrence Berkeley National Laboratory, United States
14 publications · 16.4K citations
14
h-index
12
h-index
11
h-index
10
h-index
10
h-index
1–5 of 2.2K
698
Northwestern University
Evanston, United States
20 h-index
61 publications
27.2K citations

Lawrence Berkeley National Laboratory
Berkeley, United States
20 h-index
88 publications
160.8K citations
National Institute of Standards and Technology
Gaithersburg, United States
15 h-index
67 publications
14.1K citations
Argonne National Laboratory
Lemont, United States
15 h-index
30 publications
5.5K citations
Massachusetts Institute of Technology
Cambridge, United States
15 h-index
39 publications
144.3K citations

Shanghai University
Shanghai, China
15 h-index
64 publications
5.9K citations

University of Science and Technology Beijing
Beijing, China
14 h-index
63 publications
4.4K citations
University of California, Berkeley
Berkeley, United States
13 h-index
43 publications
8.3K citations
1–8 of 698
29 publications · 5.6K citations
26
h-index
22 publications · 4K citations
19
h-index
12 publications · 1.5K citations
12
h-index
11 publications · 323 citations
11
h-index
10 publications · 1.2K citations
10
h-index
1–5