Leaderboards/machine learning in materials science

machine learning in materials science

Last updated June 11, 2026

Rising Stars

New and hot

Rising Stars Researchers

1
Yeonghun Kang
Korea Advanced Institute of Science and Technology
99
2
Jonathan Schmidt
ETH Zurich
60
3
Li Zhang
Chinese University of Hong Kong
30
4
Yan Cao
Xi'an Technological University
20
5
Mahdi Ghadiri
Duy Tan University
10

Rising Stars Papers

1
The pathogenicity of vancomycin-resistant Enterococcus faecalis to colon cancer cells
BMC Infectious Diseases|2024
11 cit.
2
KPV and RAPA Self‐Assembled into Carrier‐Free Nanodrugs for Vascular Calcification Therapy
Advanced Healthcare Materials|2024
2 cit.

Emerging Leaders

Gaining momentum

Emerging Leaders Researchers

1
Kevin Maik Jablonka
Helmholtz Institute Jena
99
2
Jinzhe Zeng
Rutgers, The State University of New Jersey
90
3
Michael C. Yip
University of California San Diego
80
4
Sue Sin Chong
Xiamen University Malaysia
70
5
Ilyes Batatia
University of Cambridge
70
6
Shuhao Zhang
Los Alamos National Laboratory
60
7
Salah A. Faroughi
Texas State University
50
8
Ali Lashkaripour
Stanford University
40
9
Benedetta Flebus
Boston College
30
10
Nikita V. Muravyev
Semenov Institute of Chemical Physics
20

Emerging Leaders Papers

Insufficient data

All-Time Leaders

The canon

All-Time Leaders Researchers

1
Steven J. Plimpton
Sandia National Laboratories
99
2
Miguel A. L. Marques
Martin Luther University Halle-Wittenberg
90
3
Jihan Kim
Korea Advanced Institute of Science and Technology
90
4
Ming Lei
Beijing University of Posts and Telecommunications
80
5
Gábor Cśanyi
Bridge University
80
6
Adam S. Foster
Kanazawa University
70
7
Anubhav Jain
Lawrence Berkeley National Laboratory
60
8
Wencong Lu
Shanghai University
50
9
Siqi Shi
Shanghai University
40
10
Lidong Yang
Hong Kong Polytechnic University
40

All-Time Leaders Papers

1
Recent advances and applications of machine learning in solid-state materials science
npj Computational Materials|2019
2.4k cit.
2
Unsupervised word embeddings capture latent knowledge from materials science literature
Nature|2019
1.1k cit.
3
Machine Learning Interatomic Potentials as Emerging Tools for Materials Science
Advanced Materials|2019
959 cit.
4
DScribe: Library of descriptors for machine learning in materials science
Computer Physics Communications|2019
794 cit.
5
Vacancy and interstitial defects in hafnia
Physical review. B, Condensed matter|2002
637 cit.
6
Mechanism of atomic force microscopy imaging of three-dimensional hydration structures at a solid-liquid interface
Physical Review B|2015
126 cit.
7
Gaussian approximation potential modeling of lithium intercalation in carbon nanostructures.
Apollo (University of Cambridge)|2018
52 cit.
8
Emergence of superconductivity in doped H2O ice at high pressure
Scientific Reports|2017
26 cit.

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