Standout Papers
- Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons (2010)
- On representing chemical environments (2013)
- Gaussian Process Regression for Materials and Molecules (2021)
- Comparing molecules and solids across structural and alchemical space (2016)
- Machine learning unifies the modeling of materials and molecules (2017)
G aussian approximation potentials: A brief tutorial introduction (2015)- Machine Learning a General-Purpose Interatomic Potential for Silicon (2018)
- Modeling Molecular Interactions in Water: From Pairwise to Many-Body Potential Energy Functions (2016)
Immediate Impact
3 by Nobel laureates 15 from Science/Nature 317 standout
Citing Papers
MACE-OFF: Short-Range Transferable Machine Learning Force Fields for Organic Molecules
2025 Standout
Hydrogen Embrittlement as a Conspicuous Material Challenge─Comprehensive Review and Future Directions
2024 Standout
Works of Albert P. Bartók being referenced
Gaussian Process Regression for Materials and Molecules
2021 Standout
Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
2010 Standout
Author Peers
| Author | Last Decade | Papers | Cites | ||||
|---|---|---|---|---|---|---|---|
| Albert P. Bartók | 6687 | 1586 | 290 | 2035 | 50 | 8.0k | |
| Matthias Rupp | 4882 | 1000 | 58 | 3081 | 48 | 6.0k | |
| Michele Ceriotti | 6294 | 3751 | 418 | 2024 | 165 | 10.4k | |
| O. Anatole von Lilienfeld | 8041 | 2830 | 155 | 4327 | 115 | 10.6k | |
| Noam Bernstein | 5630 | 1709 | 193 | 800 | 112 | 8.1k | |
| Jörg Behler | 12518 | 4106 | 640 | 3375 | 116 | 14.9k | |
| Risi Kondor | 3474 | 605 | 100 | 1160 | 28 | 4.8k | |
| Justin S. Smith | 3338 | 682 | 37 | 2048 | 59 | 4.6k | |
| Kristof T. Schütt | 3775 | 590 | 44 | 1988 | 21 | 4.5k | |
| Aidan P. Thompson | 6796 | 1440 | 444 | 471 | 82 | 11.0k | |
| Gábor Cśanyi | 14020 | 3096 | 454 | 3529 | 168 | 17.2k |
All Works
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