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Abstract Materials science evolves to a state where the composition and structure of a crystal can be controlled almost at will. Given that a composition meets basic requirements of stoichiometry, steric demands, and charge neutrality, researchers are now able to investigate a wide range of compounds theoretically and, under various experimental conditions, select the constituting fragments of a crystal. One intriguing playground for such materials design is the perovskite structure. While a game of mixing and matching ions has been played successfully for about 150 years within the limits of inorganic compounds, the recent advances in organic–inorganic hybrid perovskite photovoltaics have triggered the inclusion of organic ions. Organic ions can be incorporated on all sites of the perovskite structure, leading to hybrid (double, triple, etc.) perovskites and inverse (hybrid) perovskites. Examples for each of these cases are known, even with all three sites occupied by organic molecules. While this change from monatomic ions to molecular species is accompanied with increased complexity, it shows that concepts from traditional inorganic perovskites are transferable to the novel hybrid materials. The increased compositional space holds promising new possibilities and applications for the universe of perovskite materials.
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Organic crystal structure prediction and its application to materials design
Abstract In recent years, substantial progress has been made in the modeling of organic solids. Computer simulation has been increasingly shaping the area of new organic materials by design. It is possible to discover new organic crystals by computational structure prediction, based on the combination of powerful exploratory algorithms and accurate energy modeling. In this review, we begin with several key early concepts in describing crystal packing, and then introduce the recent state-of-the-art computational techniques for organic crystal structure prediction. Perspectives on the remaining technical challenges, functional materials screening and software development are also discussed in the end. It is reasonable to expect that, in the near future, accurate predictive computational modeling can be accomplished within a time frame that is appreciably shorter than that needed for the laboratory synthesis and characterization. Graphical abstract
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- PAR ID:
- 10371538
- Publisher / Repository:
- Cambridge University Press (CUP)
- Date Published:
- Journal Name:
- Journal of Materials Research
- Volume:
- 38
- Issue:
- 1
- ISSN:
- 0884-2914
- Page Range / eLocation ID:
- p. 19-36
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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