The DOI matches its Crossref record; 172 of the 172 references it cites were found in a registry or an index.
Machine Learning a General-Purpose Interatomic Potential for Silicon
Open accessAbstract
A machine-learning based approach to computing the energy and forces of silicon atoms circumvents the need for complex electronic structure calculations, offering an efficient method for predicting material properties that can be extended to other materials.
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
Special points for Brillouin-zone integrations
Monkhorst, H. J., & Pack, J. D. (1976) · Physical Review B · doi:10.1103/physrevb.13.5188 · Cited by 71,721
Fast Parallel Algorithms for Short-Range Molecular Dynamics
Plimpton, S. (1995) · Journal of Computational Physics · doi:10.1006/jcph.1995.1039 · Cited by 44,721
Wu, Y. (1999) · Technometrics · doi:10.1080/00401706.1999.10485951 · Cited by 27,221
Perdew, J. P., Chevary, J. A., Vosko, S. H., Jackson, K. A., Pederson, M. R., Singh, D. J., & Fiolhais, C. (1992) · Physical Review B · doi:10.1103/physrevb.46.6671 · Cited by 22,269
Perdew, J. P., Chevary, J. A., Vosko, S. H., Jackson, K. A., Pederson, M. R., Singh, D. J., & Fiolhais, C. (1993) · Physical Review B · doi:10.1103/physrevb.48.4978.2 · Cited by 17,144
Advances in Neural Information Processing Systems 19
(2007) · The MIT Press eBooks · doi:10.52202/085713 · Cited by 15,539
First principles methods using CASTEP
Clark, S. J., Segall, M. D., Pickard, C. J., Hasnip, P. J., Probert, M. I. J., Refson, K., & Payne, M. C. (2005) · Zeitschrift für Kristallographie - Crystalline Materials · doi:10.1524/zkri.220.5.567.65075 · Cited by 14,633
First principles phonon calculations in materials science
Togo, A., & Tanaka, I. (2015) · Scripta Materialia · doi:10.1016/j.scriptamat.2015.07.021 · Cited by 11,549
Unified Approach for Molecular Dynamics and Density-Functional Theory
Car, R., & Parrinello, M. (1985) · Physical Review Letters · doi:10.1103/physrevlett.55.2471 · Cited by 10,754
Pattern Recognition and Machine Learning
(2006) · doi:10.1007/978-0-387-45528-0 · Cited by 9,824
Cited by
Unke, O. T., Chmiela, S., Sauceda, H. E., Gastegger, M., Poltavsky, I., Schütt, K. T., Tkatchenko, A., & Müller, K.-R. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c01111 · Cited by 1,647
Performance and Cost Assessment of Machine Learning Interatomic Potentials
Zuo, Y., Chen, C., Li, X., Deng, Z., Chen, Y., Behler, J., Csányi, G., Shapeev, A. V., Thompson, A. P., Wood, M. A., et al. (2020) · The Journal of Physical Chemistry A · doi:10.1021/acs.jpca.9b08723 · Cited by 968
Self-citationFan, Z., Zeng, Z., Zhang, C., Wang, Y., Song, K., Dong, H., Chen, Y., & Ala-Nissila, T. (2021) · Physical Review B · doi:10.1103/physrevb.104.104309 · Cited by 528
The Open Catalyst 2022 (OC22) Dataset and Challenges for Oxide Electrocatalysts
Tran, R., Lan, J., Shuaibi, M., Wood, B. M., Goyal, S., Das, A., Heras-Domingo, J., Kolluru, A., Rizvi, A., Shoghi, N., et al. (2023) · ACS Catalysis · doi:10.1021/acscatal.2c05426 · Cited by 329
Vandermause, J., Xie, Y., Lim, J. S., Owen, C. J., & Kozinsky, B. (2022) · Nature Communications · doi:10.1038/s41467-022-32294-0 · Cited by 147
Metadata
- Authors
- Bartók, A. P., Kermode, J., Bernstein, N., & Csányi, G.
- Venue
- Physical Review X
- Publisher
- American Physical Society (APS)
- Published
- 2018-12-14
- Volume
- 8
- Issue
- 4
- ISSN
- 2160-3308 (ISSN-L)
- OpenAlex
- W2800301423
- Cited by
- 470
- References
- 172
- FWCI
- 15.36
- Type
- journal article
- Language
- en
- Open access
- gold
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: Machine Learning a General-Purpose Interatomic Potential for Silicon. https://app.cochat.ai/papers/10.1103/physrevx.8.041048
- The paper
- Bartók, A. P., Kermode, J., Bernstein, N., & Csányi, G. (2018). Machine Learning a General-Purpose Interatomic Potential for Silicon. Physical Review X. https://doi.org/10.1103/physrevx.8.041048
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.
