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Which nets are the most common? Reticular chemistry and information entropy : CrystEngComm. / Krivovichev, S.V.
In: Crystengcomm, Vol. 26, No. 9, 2024, p. 1245-1251.Research output: Contribution to journal › Article › peer-review
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TY - JOUR
T1 - Which nets are the most common? Reticular chemistry and information entropy
T2 - CrystEngComm
AU - Krivovichev, S.V.
N1 - Export Date: 4 March 2024 CODEN: CRECF Адрес для корреспонденции: Krivovichev, S.V.; Nanomaterials Research Centre, Fersmana 14, Russian Federation; эл. почта: s.krivovichev@ksc.ru Сведения о финансировании: Russian Science Foundation, RSF, 19-17-00038 Текст о финансировании 1: I am grateful to Igor Baburin for help with the TOPOS calculations. The research was supported by the Russian Science Foundation (grant 19-17-00038). Пристатейные ссылки: Wells, A.F., (1954) Acta Crystallogr., 7, pp. 535-544; Wells, A.F., (1954) Acta Crystallogr., 7, pp. 545-554; Wells, A.F., (1954) Acta Crystallogr., 7, pp. 842-848; Wells, A.F., (1954) Acta Crystallogr., 7, pp. 849-853; Wells, A.F., (1955) Acta Crystallogr., 8, pp. 32-36; Wells, A.F., (1956) Acta Crystallogr., 9, pp. 23-28; (2007) Atlas of Zeolite Framework Types, , ed. C. Baerlocher L. B. McCusker and D. H. Olson Elsevier Science B.V. Amsterdam 6th edn; Yaghi, O.M., O'Keeffe, M., Ockwig, N.W., Chae, H.K., Eddaoudi, M., Kim, J., (2003) Nature, 423, pp. 705-714; Tranchemontagne, D.J., Ni, Z., O'Keeffe, M., Yaghi, O.M., (2008) Angew. Chem., Int. Ed., 47, pp. 5136-5147; O'Keeffe, M., (2009) Chem. Soc. Rev., 38, pp. 1215-1217; Delgado-Friedrichs, O., Hyde, S.T., O'Keeffe, M., Yaghi, O.M., (2017) Struct. Chem., 28, pp. 39-44; Howarth, A.J., Li, P., Farha, O.K., O'Keeffe, M., (2018) Cryst. Growth Des., 18, pp. 449-455; Jiang, H., Jia, J., Shkurenko, A., Chen, Z., Adil, K., Belmabkhout, Y., Weselinski, L.J., Eddaoudi, M., (2018) J. Am. Chem. Soc., 140, pp. 8858-8867; Liu, Y., O'Keeffe, M., (2018) Isr. J. Chem., 58, pp. 962-970; Chen, Z., Thiam, Z., Shkurenko, A., Weselinski, L.J., Adil, K., Jiang, H., Alezi, D., Eddaoudi, M., (2019) J. Am. Chem. Soc., 141, pp. 20480-20489; Gropp, C., Canossa, S., Wuttke, S., Gándara, F., Li, Q., Gagliardi, L., Yaghi, O.M., (2020) ACS Cent. Sci., 6, pp. 1255-1273; Delgado-Friedrichs, O., O'Keeffe, M., (2005) J. Solid State Chem., 178, pp. 2480-2485; Delgado-Friedrichs, O., O'Keeffe, M., Yaghi, O.M., (2007) Phys. Chem. Chem. Phys., 9, pp. 1035-1043; Batten, S.R., Champness, N.R., Chen, X.-M., Garcia-Martinez, J., Kitagawa, S., Öhrström, L., O'Keeffe, M., Reedijk, J., (2012) CrystEngComm, 14, pp. 3001-3004; Bonneau, C., O'Keeffe, M., Proserpio, D.M., Blatov, V.A., Batten, S.R., Bourne, S.A., Lah, M.S., Öhrström, L., (2018) Cryst. Growth Des., 18, pp. 3411-3418; O'Keeffe, M., Peskov, M.A., Ramsden, S.J., Yaghi, O.M., (2008) Acc. Chem. Res., 41, pp. 1782-1789; Ockwig, N.W., Delgado-Friedrichs, O., O'Keeffe, M., Yaghi, O.M., (2005) Acc. Chem. Res., 38, pp. 176-182; Li, M., Li, D., O'Keeffe, M., Yaghi, O.M., (2014) Chem. Rev., 114, pp. 1343-1370; Alexandrov, E.V., Blatov, V.A., Kochetkov, A.V., Proserpio, D.M., (2011) CrystEngComm, 13, pp. 3947-3958; Pauling, L., (1929) J. Am. Chem. Soc., 51, pp. 1010-1026; Krivovichev, S.V., (2012) Acta Crystallogr., Sect. A: Found. Crystallogr., 68, pp. 393-398; Krivovichev, S.V., (2013) Microporous Mesoporous Mater., 171, pp. 223-229; Krivovichev, S.V., (2013) Mineral. Mag., 77, pp. 275-326; Krivovichev, S.V., (2014) Angew. Chem., Int. Ed., 53, pp. 654-661; Krivovichev, S.V., (2018) Z. Kristallogr., 233, pp. 155-161; Krivovichev, S.V., (2021) Crystals, 11, p. 1472; Blatov, V.A., Shevchenko, A.P., Proserpio, D.M., (2014) Cryst. Growth Des., 14, pp. 3576-3586; Krivovichev, S.V., Krivovichev, V.G., Hazen, R.M., Aksenov, S.M., Avdontceva, M.S., Banaru, A.M., Gorelova, L.A., Starova, G.L., (2022) Mineral. Mag., 86, pp. 183-204; Baburin, I.A., Blatov, V.A., (2007) Acta Crystallogr., Sect. A: Found. Crystallogr., 63, pp. 791-802; Baburin, I.A., (2008) Z. Kristallogr., 223, pp. 371-381; (1990) Maxwell's Demon: Entropy, Information, Computing, , Maxwell's Demon Entropy Information Computing ed. H. S. Leff and A. F. Rex Princeton University Press Princeton NJ; Krivovichev, S.V., (2016) Acta Crystallogr., Sect. A: Found. Adv, 72, pp. 274-276; Fultz, B., (2020) Phase Transitions in Materials, , Cambridge University Press Cambridge 2nd edn; Krivovichev, S.V., (2004) Acta Crystallogr., Sect. A: Found. Crystallogr., 60, pp. 257-262; Krivovichev, S.V., (2010) Eur. J. Inorg. Chem., pp. 2594-2603; Krivovichev, S.V., Shcherbakova, E.P., Nishanbaev, T.P., (2012) Can. Mineral., 50, pp. 585-592; Krivovichev, S.V., (2012) Crystallogr. Rep., 57, pp. 10-17; Krivovichev, S.V., (2014) Mineral. Mag., 78, pp. 415-435
PY - 2024
Y1 - 2024
N2 - The information-entropy measures of the complexity of nets are proposed that take into consideration both the vertices and edges of nets. The parameters quantify the amount of Shannon information per element (vertex or edge) and per the translationally independent parts of the nets. The nets can be classified according to their complexity into very simple (0-20 bit per cell), simple (20-100 bit per cell), intermediate (100-500 bit per cell), complex (500-1000 bit per cell), and very complex (>1000 bit per cell). The information entropies for 1936 3D nets were calculated and analysed, showing that the simplest (information-poor) nets possess the lowest transitivities. The majority of the most common nets in metal-organic frameworks (MOFs) are the simplest nets within their respective categories (i.e. among the nets with the same coordination of vertices). Since the information is directly related to entropy, which is understood as a statistical parameter, the preference of the simplest nets in MOFs is at least in part governed by their low information contents and high configurational entropies. © 2024 The Royal Society of Chemistry
AB - The information-entropy measures of the complexity of nets are proposed that take into consideration both the vertices and edges of nets. The parameters quantify the amount of Shannon information per element (vertex or edge) and per the translationally independent parts of the nets. The nets can be classified according to their complexity into very simple (0-20 bit per cell), simple (20-100 bit per cell), intermediate (100-500 bit per cell), complex (500-1000 bit per cell), and very complex (>1000 bit per cell). The information entropies for 1936 3D nets were calculated and analysed, showing that the simplest (information-poor) nets possess the lowest transitivities. The majority of the most common nets in metal-organic frameworks (MOFs) are the simplest nets within their respective categories (i.e. among the nets with the same coordination of vertices). Since the information is directly related to entropy, which is understood as a statistical parameter, the preference of the simplest nets in MOFs is at least in part governed by their low information contents and high configurational entropies. © 2024 The Royal Society of Chemistry
KW - Cytology
KW - Organometallics
KW - Cell complexes
KW - Classifieds
KW - Entropy measure
KW - Information contents
KW - Information entropy
KW - Metalorganic frameworks (MOFs)
KW - Shannon information
KW - Simple informations
KW - Simple++
KW - Statistical parameters
KW - Cells
UR - https://www.mendeley.com/catalogue/df085f82-e9d7-36a7-9512-efb5af12bd06/
U2 - 10.1039/d3ce01230a
DO - 10.1039/d3ce01230a
M3 - статья
VL - 26
SP - 1245
EP - 1251
JO - CrystEngComm
JF - CrystEngComm
SN - 1466-8033
IS - 9
ER -
ID: 117319889