Organic molecular crystal with a high ultra-deep-blue emission efficiency of ∼85% for low-threshold laser

Zhuo, Zhiqiang; Wei, Chuanxin; Ni, Mingjian; Cai, Jiangli; Bai, Lubing; Zhang, He; Zhao, Qiang; Sun, Lili; Lin, Jinyi; Liu, Wei; Ding, Xue-Hua; Shen, Kang; Huang, Wei

doi:10.1016/j.dyepig.2022.110425

Cited by 11 publications

(6 citation statements)

References 65 publications

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“…10 Using the naphthalene crystal (referred as NAPHTA in the Cambridge Crystal Database) as an example, a molecule can be generally considered to have 16 neighbors (see Figure 2). Based on the intermolecular interactions, these molecules can divided into four tiers, colored in yellow (6), green (6), light blue (2), and deep blue (2). The last tier is often omitted because their interactions are much weaker.…”

Section: ■ Computational Methodologymentioning

confidence: 99%

“…Molecular solids have been extensively used as key components in chemical industries such as medicine, fertilizers, dyes, pesticides, and high energy explosives, as well as the electronics industry, since the discovery of bulk conductivity in polycyclic aromatic compounds in 1950s . While some molecules can aggregate with no particular order, such as amorphous solids, most organic solids are crystalline, and their physical properties largely depend on regularly repeating intermolecular packing.…”

Section: Introductionmentioning

confidence: 99%

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Quantification of Crystal Packing Similarity from Spherical Harmonic Transform

Zhu

Tang

Hattori³

2022

Crystal Growth & Design

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In this work, we present a new computational approach to characterize and classify molecular packing in the solid states. The key idea is to project each neighboring molecule (or short contact) from the centered molecule into a unit sphere according to the interaction energy. Consequently, the similarity between two spherical images can be evaluated from the spherical harmonics expansion based on the maximum cross-correlation. We apply this approach to successfully reproduce the previous packing assignment on a small amount of data with an improved categorization. Furthermore, we conduct a packing similarity analysis over 2000 hydrocarbon crystal data sets and uncover a set of abundant packing motifs. Unlike the previous approaches based on the subjective visual comparison at the real space, our approach provides a more robust way to measure the packing similarity, thus paving the way for a rapid classification of large scale crystal data.

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Section: ■ Computational Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Quantification of Crystal Packing Similarity from Spherical Harmonic Transform

Zhu

Tang

Hattori³

2022

Crystal Growth & Design

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show abstract

“…2). Based on the intermolecular interactions, these molecules can divided to four tiers, colored in yellow (6), green (6), light blue (2) and deep blue (2). The last tier is often omitted because their interactions are much weaker.…”

Section: A Image Representation Of Molecular Packingmentioning

confidence: 99%

Quantification of Crystal Packing Similarity from Spherical Harmonic Transform

Zhu¹,

Tang²,

Hattori³

2022

Preprint

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In this work, we present a new computational approach to characterize and classify molecular packing in the solid states.The key idea is to project each neighboring molecule (or short contact) from the centered molecule into a unit sphere according to the interaction energy. Consequently, the similarity between two spherical images can be evaluated from the spherical harmonics expansion based on the maximum cross-correlation. We apply this approach to successfully reproduce the previous packing assignment on a small data with an improved categorization. Furthermore, we conduct a packing similarity analysis over 2000 hydrocarbons crystal data sets and uncover a set of abundant packing motifs. Unlike the previous approaches based on the subjective visual comparison at the real space, our spherical harmonics expansion provides a more robust way to measure the packing similarity, thus paving the way for a rapid classification of large scale crystal data.

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“…Molecular solids refer to substances consisting of discrete molecules that are held together by relatively weak intermolecular forces. For centuries, they have been extensively used as key components in chemical industries such as medicine [1], fertilizers, dyes [2], pesticides [3], and high-energy explosives [4]. In addition, some organic solids were found to exhibit interesting physical properties.…”

Section: Introductionmentioning

confidence: 99%

Organic crystal structure prediction and its application to materials design

Zhu

Hattori²

2022

Journal of Materials Research

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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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Organic molecular crystal with a high ultra-deep-blue emission efficiency of ∼85% for low-threshold laser

Cited by 11 publications

References 65 publications

Quantification of Crystal Packing Similarity from Spherical Harmonic Transform

Quantification of Crystal Packing Similarity from Spherical Harmonic Transform

Quantification of Crystal Packing Similarity from Spherical Harmonic Transform

Organic crystal structure prediction and its application to materials design

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