Leaderboards/Machine Learning in Materials Science

Machine Learning in Materials Science

Last updated July 15, 2026

Rising Stars

Fresh ink, massive impact

Researchers

1
Guo‐Li Xiong
King University
2
Claudio Zeni
Microsoft Research (United Kingdom)
3
Amil Merchant
Google DeepMind (United Kingdom)
4
John Dagdelen
Lawrence Berkeley National Laboratory
5
Andres M. Bran
École Polytechnique Fédérale de Lausanne
6
Dávid Péter Kovács
University of Cambridge
7
Yeonghun Kang
University of Toronto
8
Hanqun Cao
University of Oxford
9
Ilyes Batatia
University of Cambridge
10
Amit Gangwal
Shri Vile Parle Kelavani Mandal

Papers

1
Structured information extraction from scientific text with large language models
Nature Communications|2024
586 cit.
2
Augmenting large language models with chemistry tools
Nature Machine Intelligence|2024
581 cit.
3
Publication in IEEE Transactions on Knowledge and Data Engineering (2024) [452 citations, W4391468387]
IEEE Transactions on Knowledge and Data Engineering|2024
452 cit.
4
A generative model for inorganic materials design
Nature|2025
367 cit.
5
ChatMOF: an artificial intelligence system for predicting and generating metal-organic frameworks using large language models
Nature Communications|2024
194 cit.
6
Publication in The Journal of Chemical Physics (2025) [186 citations, W4416191329]
The Journal of Chemical Physics|2025
186 cit.
7
MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules
Journal of the American Chemical Society|2025
160 cit.
8
Unleashing the power of generative AI in drug discovery
Drug Discovery Today|2024
151 cit.
9
Generative artificial intelligence in drug discovery: basic framework, recent advances, challenges, and opportunities
Frontiers in Pharmacology|2024
148 cit.
10
Harnessing Large Language Models to Collect and Analyze Metal–Organic Framework Property Data Set
Journal of the American Chemical Society|2025
77 cit.

Emerging Leaders

Drafting the new consensus

Researchers

1
Stefano Forli
Scripps Research Institute
2
Zachary W. Ulissi
Meta (Israel)
3
Connor W. Coley
Massachusetts Institute of Technology
4
Patrick Riley
Relay Therapeutics (United States)
5
Sorelle A. Friedler
Haverford College
6
Amir Barati Farimani
Carnegie Mellon University
7
Esben Jannik Bjerrum
Chemi (Italy)
8
Volker L. Deringer
University of Oxford
9
Jan‐Michael Mewes
Heidelberg University
10
Ekin D. Cubuk
Google (United States)

Papers

1
Scaling deep learning for materials discovery
Nature|2023
1.2k cit.
2
Molecular contrastive learning of representations via graph neural networks
Nature Machine Intelligence|2022
799 cit.
3
TransPolymer: a Transformer-based language model for polymer property predictions
npj Computational Materials|2023
187 cit.
4
A physics-informed diffusion model for high-fidelity flow field reconstruction
Journal of Computational Physics|2023
180 cit.
5
MOFormer: Self-Supervised Transformer Model for Metal–Organic Framework Property Prediction
Journal of the American Chemical Society|2023
156 cit.
6
MeltpoolNet: Melt pool characteristic prediction in Metal Additive Manufacturing using machine learning
Additive manufacturing|2022
139 cit.
7
Adaptive grey wolf optimizer
Neural Computing and Applications|2022
124 cit.
8
Machine learning modeling of family wide enzyme-substrate specificity screens
PLoS Computational Biology|2022
117 cit.
9
How to validate machine-learned interatomic potentials
The Journal of Chemical Physics|2023
116 cit.
10
Device-scale atomistic modelling of phase-change memory materials
Nature Electronics|2023
112 cit.

Current Senior Leaders

Wrote the book, still writing it

Researchers

1
Donald G. Truhlar
University of Minnesota System
2
Thomas E. Ferrin
University of California, San Francisco
3
Arthur J. Olson
Scripps Research Institute
4
Alex Zunger
National Laboratory of the Rockies
5
Jens K. Nørskov
Technical University of Denmark
6
Omar M. Yaghi
University of California, Berkeley
7
Gerbrand Ceder
Lawrence Berkeley National Laboratory
8
Weitao Yang
Duke University
9
Kristin A. Persson
Lawrence Berkeley National Laboratory
10
Erik Lindahl
Linköping University

Papers

1
UCSF Chimera—A visualization system for exploratory research and analysis
Journal of Computational Chemistry|2004
47.8k cit.
2
AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading
Journal of Computational Chemistry|2009
37.1k cit.
3
The M06 suite of density functionals for main group thermochemistry, thermochemical kinetics, noncovalent interactions, excited states, and transition elements: two new functionals and systematic testing of four M06-class functionals and 12 other functionals
Theoretical Chemistry Accounts|2007
30k cit.
4
GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers
SoftwareX|2015
26.8k cit.
5
AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility
Journal of Computational Chemistry|2009
25k cit.
6
Self-interaction correction to density-functional approximations for many-electron systems
Physical review. B, Condensed matter|1981
20.8k cit.
7
Universal Solvation Model Based on Solute Electron Density and on a Continuum Model of the Solvent Defined by the Bulk Dielectric Constant and Atomic Surface Tensions
The Journal of Physical Chemistry B|2009
17.4k cit.
8
The Chemistry and Applications of Metal-Organic Frameworks
Science|2013
16.5k cit.
9
GROMACS 4:  Algorithms for Highly Efficient, Load-Balanced, and Scalable Molecular Simulation
Journal of Chemical Theory and Computation|2008
16k cit.
10
Origin of the Overpotential for Oxygen Reduction at a Fuel-Cell Cathode
The Journal of Physical Chemistry B|2004
12.7k cit.

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