The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Metadynamics Simulations of the High-Pressure Phases of Silicon Employing a High-Dimensional Neural Network Potential
Abstract
We study in a systematic way the complex sequence of the high-pressure phases of silicon obtained upon compression by combining an accurate high-dimensional neural network representation of the density-functional theory potential-energy surface with the metadynamics scheme. Starting from the thermodynamically stable diamond structure at ambient conditions we are able to identify all structural phase transitions up to the highest-pressure fcc phase at about 100 GPa. The results are in excellent agreement with experiment. The method developed promises to be of great value in the study of inorganic solids, including those having metallic phases.
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Cites
Laio, A., & Parrinello, M. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.202427399 · Cited by 5,626
Self-citationGeneralized 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
Self-citationCrystal structure prediction using ab initio evolutionary techniques: Principles and applications
Oganov, A. R., & Glass, C. W. (2006) · The Journal of Chemical Physics · doi:10.1063/1.2210932 · Cited by 2,596
Cited by
Crystal structure prediction via particle-swarm optimization
Wang, Y., Lv, J., Zhu, L., & Ma, Y. (2010) · Physical Review B · doi:10.1103/physrevb.82.094116 · Cited by 2,747
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
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
Laio, A., & Gervasio, F. L. (2008) · Reports on Progress in Physics · doi:10.1088/0034-4885/71/12/126601 · Cited by 1,619
Metadata
- Authors
- Behler, J., Martoňák, R., Donadio, D., & Parrinello, M.
- Venue
- Physical Review Letters
- Publisher
- American Physical Society (APS)
- Published
- 2008-05-05
- Volume
- 100
- Issue
- 18
- Pages
- 185501-185501
- ISSN
- 0031-9007 (ISSN-L)
- 1079-7114
- OpenAlex
- W2000957843
- Cited by
- 233
- References
- 3
- FWCI
- 9.99
- Type
- journal article
- Language
- en
- Open access
- closed
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- Behler, J., Martoňák, R., Donadio, D., & Parrinello, M. (2008). Metadynamics Simulations of the High-Pressure Phases of Silicon Employing a High-Dimensional Neural Network Potential. Physical Review Letters. https://doi.org/10.1103/physrevlett.100.185501
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