G aussian approximation potentials: A brief tutorial introduction
Open accessAbstract
We present a swift walk‐through of our recent work that uses machine learning to fit interatomic potentials based on quantum mechanical data. We describe our Gaussian approximation potentials (GAP) framework, discuss a variety of descriptors, how to train the model on total energies and derivatives, and the simultaneous use of multiple models of different complexity. We also show a small example using QUIP, the software sandbox implementation of GAP that is available for noncommercial use. © 2015 Wiley Periodicals, Inc.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Clustering can be used to select the most relevant reference configurations from a database, or to partition it into regions on which specialized models can be trained.
“Clustering can be used to select the most relevant reference configurations from a database 109 , or to partition it into regions on which specialized models can be trained. 110”
From Unsupervised machine learning in atomistic simulations, between predictions and understanding (2019) · cites this paper as [109]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Advances in Neural Information Processing Systems 19
(2007) · The MIT Press eBooks · doi:10.52202/085713 · Cited by 16,037 · see citing works
Schölkopf, B., & Smola, A. J. (2001) · The MIT Press eBooks · doi:10.7551/mitpress/4175.001.0001 · Cited by 11,934 · see citing works
Information theory, inference, and learning algorithms
(2004) · Choice Reviews Online · doi:10.5860/choice.41-5949 · Cited by 6,535 · see citing works
Computer simulation of local order in condensed phases of silicon
Stillinger, F. H., & Weber, T. A. (1985) · Physical review. B, Condensed matter · doi:10.1103/physrevb.31.5262 · Cited by 5,319 · see citing works
Generalized Neural-Network Representation of High-Dimensional Potential-Energy Surfaces
Behler, J., & Parrinello, M. (2007) · Physical Review Letters · doi:10.1103/physrevlett.98.146401 · Cited by 5,010 · see citing works
Information Theory, Inference, and Learning Algorithms
(2004) · Kybernetes · doi:10.1108/03684920410534506 · Cited by 4,937 · see citing works
Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Williams, C. K. I. (2003) · Journal of the American Statistical Association · doi:10.1198/jasa.2003.s269 · Cited by 4,390 · see citing works
Bayesian Learning for Neural Networks
Neal, R. M. (1996) · Lecture Notes in Statistics · doi:10.1007/978-1-4612-0745-0 · Cited by 4,276 · see citing works
On representing chemical environments
Bartók, A. P., Kondor, R., & Csányi, G. (2013) · Physical Review B · doi:10.1103/physrevb.87.184115 · Cited by 2,572 · see citing works
Self-citationFast and Accurate Modeling of Molecular Atomization Energies with Machine Learning
Rupp, M., Tkatchenko, A., Müller, K.-R., & von Lilienfeld, O. A. (2012) · Physical Review Letters · doi:10.1103/physrevlett.108.058301 · Cited by 2,312 · see citing works
A Unifying View of Sparse Approximate Gaussian Process Regression
Quiñonero-Candela, J., & Rasmussen, C. E. (2005) · doi:10.5555/1046920.1194909 · Cited by 1,739 · see citing works
Neural network potential-energy surfaces in chemistry: a tool for large-scale simulations
Behler, J. (2011) · Physical Chemistry Chemical Physics · doi:10.1039/c1cp21668f · Cited by 781 · see citing works
Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies
Hansen, K., Montavon, G., Biegler, F., Fazli, S., Rupp, M., Scheffler, M., von Lilienfeld, O. A., Tkatchenko, A., & Müller, K. (2013) · Journal of Chemical Theory and Computation · doi:10.1021/ct400195d · Cited by 615 · see citing works
Medders, G. R., Babin, V., & Paesani, F. (2014) · Journal of Chemical Theory and Computation · doi:10.1021/ct5004115 · Cited by 436 · see citing works
Potential Energy Surfaces Fitted by Artificial Neural Networks
Handley, C. M., & Popelier, P. L. A. (2010) · The Journal of Physical Chemistry A · doi:10.1021/jp9105585 · Cited by 328 · see citing works
Accuracy and transferability of Gaussian approximation potential models for tungsten
Szlachta, W. J., Bartók, A. P., & Cśanyi, G. (2014) · Physical Review B · doi:10.1103/physrevb.90.104108 · Cited by 318 · see citing works
Self-citationClassical and quasiclassical spectral analysis of CH5+ using an ab initio potential energy surface
Brown, A., Braams, B. J., Christoffel, K. M., Jin, Z., & Bowman, J. M. (2003) · The Journal of Chemical Physics · doi:10.1063/1.1622379 · Cited by 109 · see citing works
First-principles energetics of water clusters and ice: A many-body analysis
Gillan, M. J., Alfè, D., Bartók, A. P., et al. (2013) · The Journal of Chemical Physics · doi:10.1063/1.4852182 · Cited by 43 · see citing works
Self-citation
Flagged first, then by citations
Research works printed but not linked: 2
- [9]9. C. E. Rasmussen and C. K. I. Williams, Gaussian Processes for Machine Learning (MIT Press, 2006).
- [10]10. R. M. Neal, Ph.D. thesis, University of Toronto (1995).
Cited by
Sorted by citations
Machine learning in materials informatics: recent applications and prospects
Ramprasad, R., Batra, R., Pilania, G., Mannodi-Kanakkithodi, A., & Kim, C. (2017) · npj Computational Materials · doi:10.1038/s41524-017-0056-5 · Cited by 1,945 · see citing works
From DFT to machine learning: recent approaches to materials science–a review
Schleder, G. R., Padilha, A. C. M., Acosta, C. M., Costa, M., & Fazzio, A. (2019) · Journal of Physics: Materials · doi:10.1088/2515-7639/ab084b · Cited by 852 · see citing works
Machine learning molecular dynamics for the simulation of infrared spectra
Gastegger, M., Behler, J., & Marquetand, P. (2017) · Chemical Science · doi:10.1039/c7sc02267k · Cited by 556 · see citing works
Symmetry-Adapted Machine Learning for Tensorial Properties of Atomistic Systems
Grisafi, A., Wilkins, D. M., Csányi, G., & Ceriotti, M. (2018) · Physical Review Letters · doi:10.1103/physrevlett.120.036002 · Cited by 309 · see citing works
Imbalzano, G., Anelli, A., Giofré, D., Klees, S., Behler, J., & Ceriotti, M. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5024611 · Cited by 330 · see citing works
Unsupervised machine learning in atomistic simulations, between predictions and understanding
Ceriotti, M. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5091842 · Cited by 173 · see citing works
Nguyen, T. T., Székely, E., Imbalzano, G., Behler, J., Csányi, G., Ceriotti, M., Götz, A. W., & Paesani, F. (2018) · The Journal of Chemical Physics · doi:10.1063/1.5024577 · Cited by 171 · see citing works
Self-citationZhao, R., Xing, B., Mu, H., Fu, Y., & Zhang, L. (2022) · Chinese Physics B · doi:10.1088/1674-1056/ac5d2d · Cited by 21 · see citing works
- Authors
- Bartók, A. P., & Csányi, G.
- Affiliations
- University of Cambridge (GB)
- Engineering Laboratory, University of Cambridge Trumpington Street Cambridge CB2 1PZ United Kingdom
- Corresponding author
- Bartók, Albert P.
- ORCID
- Bartók, Albert P.
- Venue
- International Journal of Quantum Chemistry
- Publisher
- Wiley
- Published
- 2015-04-27
- Volume
- 115
- Issue
- 16
- Pages
- 1051-1057
- ISSN
- 0020-7608 (ISSN-L)
- 1097-461X
- OpenAlex
- W1978183953
- Cited by
- 617
- References
- 18
- Citations by year
- 2026: 37 · 2025: 56 · 2024: 64 · 2023: 64 · 2022: 76 · 2021: 79 · 2020: 92 · 2019: 54 · 2018: 48 · 2017: 34 · 2016: 11 · 2015: 2
- FWCI
- 12.83
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Journal of Quantum Chemistry (publishedVersion)
- Warwick Research Archive Portal (University of Warwick) (acceptedVersion)
- Apollo (University of Cambridge) (acceptedVersion)
- Funders
- Leverhulme Early Career Fellowship
- Isaac Newton Trust
- Topics
- Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
- Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
- Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Model Reduction and Neural Networks
- Keywords
- Gaussian approximation potential, interatomic potentials, machine learning interatomic potentials, atomic descriptors, QUIP
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
