The DOI matches its Crossref record; 87 of the 87 references it cites were found in a registry or an index.
An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO2
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Citations
Cites
Generalized Gradient Approximation Made Simple
Perdew, J. P., Burke, K., & Ernzerhof, M. (1996) · Physical Review Letters · doi:10.1103/physrevlett.77.3865 · Cited by 215,824
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An Algorithm for Least-Squares Estimation of Nonlinear Parameters
Marquardt, D. W. (1963) · Journal of the Society for Industrial and Applied Mathematics · doi:10.1137/0111030 · Cited by 30,728
QUANTUM ESPRESSO: a modular and open-source software project for quantum simulations of materials
Giannozzi, P., Baroni, S., Bonini, N., Calandra, M., Car, R., Cavazzoni, C., Ceresoli, D., Chiarotti, G. L., Cococcioni, M., Dabo, I., et al. (2009) · Journal of Physics: Condensed Matter · doi:10.1088/0953-8984/21/39/395502 · Cited by 29,281
VESTA 3 for three-dimensional visualization of crystal, volumetric and morphology data
Momma, K., & Izumi, F. (2011) · Journal of Applied Crystallography · doi:10.1107/s0021889811038970 · Cited by 26,054
Canonical sampling through velocity rescaling
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A method for the solution of certain non-linear problems in least squares
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The surface science of titanium dioxide
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Cited by
Recent advances and applications of deep learning methods in materials science
Choudhary, K., DeCost, B., Chen, C., Jain, A., Tavazza, F., Cohn, R., Park, C. W., Choudhary, A., Agrawal, A., Billinge, S. J. L., et al. (2022) · npj Computational Materials · doi:10.1038/s41524-022-00734-6 · Cited by 1,172
“Such FF models have been developed for molecular systems such as water, methane and other organic molecules [102, 112] as well as solids such as silicon [111], sodium [114], graphite [115] and titania (TiO2) [116].”
BackgroundPerformance 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
Four Generations of High-Dimensional Neural Network Potentials
Behler, J. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.0c00868 · Cited by 873
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
Active learning of linearly parametrized interatomic potentials
Podryabinkin, E. V., & Shapeev, A. V. (2017) · Computational Materials Science · doi:10.1016/j.commatsci.2017.08.031 · Cited by 678
Metadata
- Authors
- Artrith, N., & Urban, A.
- Venue
- Computational Materials Science
- Publisher
- Elsevier BV
- Published
- 2016-03
- Volume
- 114
- Pages
- 135-150
- ISSN
- 0927-0256 (ISSN-L)
- OpenAlex
- W2200589053
- Cited by
- 508
- References
- 87
- FWCI
- 16.59
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO2. https://app.cochat.ai/papers/10.1016/j.commatsci.2015.11.047
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- Artrith, N., & Urban, A. (2016). An implementation of artificial neural-network potentials for atomistic materials simulations: Performance for TiO2. Computational Materials Science. https://doi.org/10.1016/j.commatsci.2015.11.047
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.
