The DOI matches its Crossref record; 10 of the 10 references it cites were found in a registry or an index.
Transferable Machine-Learning Model of the Electron Density
Open accessAbstract
The electronic charge density plays a central role in determining the behavior of matter at the atomic scale, but its computational evaluation requires demanding electronic-structure calculations. We introduce an atom-centered, symmetry-adapted framework to machine-learn the valence charge density based on a small number of reference calculations. The model is highly transferable, meaning it can be trained on electronic-structure data of small molecules and used to predict the charge density of larger compounds with low, linear-scaling cost. Applications are shown for various hydrocarbon molecules of increasing complexity and flexibility, and demonstrate the accuracy of the model when predicting the density on octane and octatetraene after training exclusively on butane and butadiene. This transferable, data-driven model can be used to interpret experiments, accelerate electronic structure calculations, and compute electrostatic interactions in molecules and condensed-phase systems.
Overview
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
Verification
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.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Machine learning for molecular and materials science
Butler, K. T., Davies, D. W., Cartwright, H., Isayev, O., & Walsh, A. (2018) · Nature · doi:10.1038/s41586-018-0337-2 · Cited by 4,968
Gaussian Approximation Potentials: The Accuracy of Quantum Mechanics, without the Electrons
Bartók, A. P., Payne, M. C., Kondor, R., & Csányi, G. (2010) · Physical Review Letters · doi:10.1103/physrevlett.104.136403 · Cited by 3,242
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
Fast 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
ANI-1: an extensible neural network potential with DFT accuracy at force field computational cost
Smith, J. S., Isayev, O., & Roitberg, A. E. (2017) · Chemical Science · doi:10.1039/c6sc05720a · Cited by 1,921
Comparing molecules and solids across structural and alchemical space
De, S., Bartók, A. P., Csányi, G., & Ceriotti, M. (2016) · Physical Chemistry Chemical Physics · doi:10.1039/c6cp00415f · Cited by 778
Self-citationImbalzano, 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
Self-citationSymmetry-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
Self-citationMachine learning for the structure–energy–property landscapes of molecular crystals
Musil, F., De, S., Yang, J., Campbell, J. E., Day, G. M., & Ceriotti, M. (2018) · Chemical Science · doi:10.1039/c7sc04665k · Cited by 216
Self-citationAccurate molecular polarizabilities with coupled cluster theory and machine learning
Wilkins, D. M., Grisafi, A., Yang, Y., Lao, K. U., DiStasio, R. A., & Ceriotti, M. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1816132116 · Cited by 204
Self-citation
Cited by
Combining Machine Learning and Computational Chemistry for Predictive Insights Into Chemical Systems
Keith, J. A., Vassilev-Galindo, V., Cheng, B., Chmiela, S., Gastegger, M., Müller, K.-R., & Tkatchenko, A. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00107 · Cited by 849
A Critical Review of Machine Learning of Energy Materials
Chen, C., Zuo, Y., Ye, W., Li, X., Deng, Z., & Ong, S. P. (2020) · Advanced Energy Materials · doi:10.1002/aenm.201903242 · Cited by 609
Emerging materials intelligence ecosystems propelled by machine learning
Batra, R., Song, L., & Ramprasad, R. (2020) · Nature Reviews Materials · doi:10.1038/s41578-020-00255-y · Cited by 446
Atom-density representations for machine learning
Willatt, M. J., Musil, F., & Ceriotti, M. (2019) · The Journal of Chemical Physics · doi:10.1063/1.5090481 · Cited by 179
Self-citationPANNA: Properties from Artificial Neural Network Architectures
Lot, R., Pellegrini, F., Shaidu, Y., & Küçükbenli, E. (2020) · Computer Physics Communications · doi:10.1016/j.cpc.2020.107402 · Cited by 64
Metadata
- Authors
- Grisafi, A., Fabrizio, A., Meyer, B., Wilkins, D. M., Corminboeuf, C., & Ceriotti, M.
- Venue
- ACS Central Science
- Publisher
- American Chemical Society (ACS)
- Published
- 2018-12-26
- Volume
- 5
- Issue
- 1
- Pages
- 57-64
- ISSN
- 2374-7943 (ISSN-L)
- 2374-7951
- OpenAlex
- W2889703828
- Cited by
- 269
- References
- 10
- FWCI
- 10.75
- Type
- journal article
- Language
- en
- Open access
- diamond
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
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Transferable Machine-Learning Model of the Electron Density. https://app.cochat.ai/papers/10.1021/acscentsci.8b00551
- The paper
- Grisafi, A., Fabrizio, A., Meyer, B., Wilkins, D. M., Corminboeuf, C., & Ceriotti, M. (2019). Transferable Machine-Learning Model of the Electron Density. ACS Central Science. https://doi.org/10.1021/acscentsci.8b00551
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
