Gaussian Approximation Potentials is a machine learning-based interatomic potential that uses Gaussian process regression to approximate quantum-mechanical potential energy surfaces by mapping atomic environments to energies with uncertainty quantification.
248
Publications
23.6K
Citations
607
Authors
328
Institutions
Publications per year
2017–2026
94
Variants
Gap
Parents
Publications
248
Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
Albert P. Bartók, M. C. Payne, Risi Kondor et al. · Physical Review Letters · 2010 · 3.1K citations · Full text
Natural Sciences, Chemistry, Physics +17
1–5 of 248
Authors
607
- GC
25
h-index
- AP
11
h-index
- VL
10
h-index
- JB
8
h-index
- GS
7
h-index
1–5 of 607
Institutions
328
University of Cambridge
Cambridge, United Kingdom
28 h-index
68 publications
27.6K citations
University of Helsinki
Helsinki, Finland
14 h-index
43 publications
1.8K citations
Argonne National Laboratory
Lemont, United States
11 h-index
43 publications
2.7K citations
Helsinki Institute of Physics
Helsinki, Finland
11 h-index
19 publications
748 citations
Aalto University
Espoo, Finland
8 h-index
19 publications
1.2K citations
University of Warwick
Coventry, United Kingdom
7 h-index
11 publications
739 citations
University College London
London, United Kingdom
6 h-index
17 publications
3.1K citations
University of Oxford
Oxford, United Kingdom
6 h-index
16 publications
1.8K citations
1–8 of 328
Venues
- The Journal of Chemical Physics
38 publications · 2.9K citations
25
h-index
- Physical review. D. Particles, fields, gravitation, and cosmology/Physical review. D. Particles and fields
11 publications · 272 citations
11
h-index
- Physical review. B./Physical review. B
PRB · 9 publications · 1.3K citations
9
h-index
- Chemical Physics Letters
7 publications · 417 citations
7
h-index
- Physical Review Materials
7 publications · 571 citations
7
h-index
1–5