The DOI matches its Crossref record; 44 of the 44 references it cites were found in a registry or an index.
Crystal structure prediction using the minima hopping method
Open accessAbstract
A structure prediction method is presented based on the minima hopping method. To escape local minima, moves on the configurational enthalpy surface are performed by variable cell shape molecular dynamics. To optimize the escape steps the initial atomic and cell velocities are aligned to low curvature directions of the current local minimum. The method is applied to both silicon crystals and well-studied binary Lennard-Jones mixtures. For the latter new putative ground state structures are presented. It is shown that a high success rate is achieved and a reliable prediction of unknown ground state structures is possible.
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Cites
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The Open Quantum Materials Database (OQMD): assessing the accuracy of DFT formation energies
Kirklin, S., Saal, J. E., Meredig, B., Thompson, A., Doak, J. W., Aykol, M., Rühl, S., & Wolverton, C. (2015) · npj Computational Materials · doi:10.1038/npjcompumats.2015.10 · Cited by 2,378
Structure prediction drives materials discovery
Oganov, A. R., Pickard, C. J., Zhu, Q., & Needs, R. J. (2019) · Nature Reviews Materials · doi:10.1038/s41578-019-0101-8 · Cited by 717
Physics-Inspired Structural Representations for Molecules and Materials
Musil, F., Grisafi, A., Bartók, A. P., Ortner, C., Csányi, G., & Ceriotti, M. (2021) · Chemical Reviews · doi:10.1021/acs.chemrev.1c00021 · Cited by 523
Machine Learning Strategy for Accelerated Design of Polymer Dielectrics
Mannodi-Kanakkithodi, A., Pilania, G., Huan, T. D., Lookman, T., & Ramprasad, R. (2016) · Scientific Reports · doi:10.1038/srep20952 · Cited by 389
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
Metadata
- Authors
- Amsler, M., & Goedecker, S.
- Venue
- The Journal of Chemical Physics
- Publisher
- AIP Publishing
- Published
- 2010-12-10
- Volume
- 133
- Issue
- 22
- Pages
- 224104-224104
- ISSN
- 0021-9606 (ISSN-L)
- 1089-7690
- OpenAlex
- W2069407997
- Cited by
- 306
- References
- 44
- FWCI
- 4.63
- Type
- journal article
- Language
- en
- Open access
- green
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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.
