The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
How to quantify energy landscapes of solids
Open accessAbstract
We explore whether the topology of energy landscapes in chemical systems obeys any rules and what these rules are. To answer this and related questions we use several tools: (i) Reduced energy surface and its density of states, (ii) descriptor of structure called fingerprint function, which can be represented as a one-dimensional function or a vector in abstract multidimensional space, (iii) definition of a “distance” between two structures enabling quantification of energy landscapes, (iv) definition of a degree of order of a structure, and (v) definitions of the quasi-entropy quantifying structural diversity. Our approach can be used for rationalizing large databases of crystal structures and for tuning computational algorithms for structure prediction. It enables quantitative and intuitive representations of energy landscapes and reappraisal of some of the traditional chemical notions and rules. Our analysis confirms the expectations that low-energy minima are clustered in compact regions of configuration space (“funnels”) and that chemical systems tend to have very few funnels, sometimes only one. This analysis can be applied to the physical properties of solids, opening new ways of discovering structure-property relations. We quantitatively demonstrate that crystals tend to adopt one of the few simplest structures consistent with their chemistry, providing a thermodynamic justification of Pauling’s fifth rule.
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Cites
Crystal 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
THE PRINCIPLES DETERMINING THE STRUCTURE OF COMPLEX IONIC CRYSTALS
Pauling, L. (1929) · Journal of the American Chemical Society · doi:10.1021/ja01379a006 · Cited by 2,025
Cited by
Recent advances and applications of machine learning in solid-state materials science
Schmidt, J., Marques, M. R. G., Botti, S., & Marques, M. A. L. (2019) · npj Computational Materials · doi:10.1038/s41524-019-0221-0 · Cited by 2,607
How Evolutionary Crystal Structure Prediction Works—and Why
Oganov, A. R., Lyakhov, A. O., & Valle, M. (2011) · Accounts of Chemical Research · doi:10.1021/ar1001318 · Cited by 1,240
Self-citationThe thermodynamic scale of inorganic crystalline metastability
Sun, W., Dacek, S. T., Ong, S. P., Hautier, G., Jain, A., Richards, W. D., Gamst, A. C., Persson, K. A., & Ceder, G. (2016) · Science Advances · doi:10.1126/sciadv.1600225 · Cited by 961
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
Self-citationSemimetallic Two-Dimensional Boron Allotrope with Massless Dirac Fermions
Zhou, X.-F., Dong, X., Oganov, A. R., Zhu, Q., Tian, Y., & Wang, H.-T. (2014) · Physical Review Letters · doi:10.1103/physrevlett.112.085502 · Cited by 638
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Metadata
- Authors
- Oganov, A. R., & Valle, M.
- Venue
- The Journal of Chemical Physics
- Publisher
- AIP Publishing
- Published
- 2009-03-10
- Volume
- 130
- Issue
- 10
- Pages
- 104504-104504
- ISSN
- 0021-9606 (ISSN-L)
- 1089-7690
- OpenAlex
- W2075245047
- Cited by
- 211
- References
- 2
- FWCI
- 3.83
- Type
- journal article
- Language
- en
- Open access
- green
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