185. Watanabe T, Sholl DS (2012) Accelerating applications of metal–organic frameworks for gas
adsorption and separation by computational screening of materials. Langmuir 28:14114–
14128
186. Barthel S, Alexandrov EV, Proserpio DM, Smit B (2018) Distinguishing metal–organic
frameworks. Cryst Growth Des 18:1738–1747
187. Miklitz M, Jelfs KE (2018) pywindow: automated structural analysis of molecular pores. J
Chem Inf Model 58:2387–2391
188. Coudert F-X, Fuchs AH (2016) Computational characterization and prediction of metal–
organic framework properties. Coord Chem Rev 307:211–236
189. Goldsmith J, Wong-Foy AG, Cafarella MJ, Siegel DJ (2013) Theoretical limits of hydrogen
storage in metal–organic frameworks: opportunities and trade-offs. Chem Mater 25:3373–
3382
190. Moghadam PZ, Islamoglu T, Goswami S, Exley J, Fantham M, Kaminski CF, Snurr RQ, Farha
OK, Fairen-Jimenez D (2018) Computer-aided discovery of a metal–organic framework with
superior oxygen uptake. Nat Commun 9:1378
191. Altintas C, Erucar I, Keskin S (2018) High-throughput computational screening of the metal
organic framework database for CH 4/H 2 separations. ACS Appl Mater Interf 10:3668–3679
192. Azar ANV, Velioglu S, Keskin S (2019) Large-scale computational screening of metal organic
framework (MOF) membranes and MOF-based polymer membranes for H 2 /N 2 separations.
ACS Sustain Chem Eng 7:9525–9536
193. Inokuma Y, Matsumura K, Yoshioka S, Fujita M (2017) Finding a new crystalline sponge
from a crystallographic database. Chem – An Asian J 12:208–211
194. Zhang L, Chen Z, Su J, Li J (2019) Data mining new energy materials from structure
databases. Renew Sust Energ Rev 107:554–567
195. Shi P-P, Tang Y-Y, Li P-F, Liao W-Q, Wang Z-X, Ye Q, Xiong R-G (2016) Symmetry
breaking in molecular ferroelectrics. Chem Soc Rev 45:3811–3827
196. Cole JM, Kreiling S (2002) Exploiting structure/property relationships in organic non-linear
optical materials: developing strategies to realize the potential of TCNQ derivatives.
CrystEngComm 4:232–238
197. Phan H, Hrudka JJ, Igimbayeva D, Lawson Daku LM, Shatruk M (2017) A simple approach
for predicting the spin state of homoleptic Fe(II) Tris-diimine complexes. J Am Chem Soc
139:6437–6447
198. Schober C, Reuter K, Oberhofer H (2016) Virtual screening for high carrier mobility in
organic semiconductors. J Phys Chem Lett 7:3973–3977
199. Kunkel C, Schober C, Oberhofer H, Reuter K (2019) Knowledge discovery through chemical
space networks: the case of organic electronics. J Mol Model 25:87
200. Cole JM, Low KS, Ozoe H, Stathi P, Kitamura C, Kurata H, Rudolf P, Kawase T (2014) Data
mining with molecular design rules identifies new class of dyes for dye-sensitised solar cells.
Phys Chem Chem Phys 16:26684–26690
201. Adalder TK, Dastidar P (2014) Crystal engineering approach toward selective formation of an
asymmetric supramolecular synthon in primary ammonium monocarboxylate (PAM) salts and
their gelation studies. Cryst Growth Des 14:2254–2262
202. Veits GK, Carter KK, Cox SJ, McNeil AJ (2016) Developing a gel-based sensor using crystal
morphology prediction. J Am Chem Soc 138:12228–12233
203. Elton DC, Boukouvalas Z, Butrico MS, Fuge MD, Chung PW (2018) Applying machine
learning techniques to predict the properties of energetic materials. Sci Rep 8:9059
204. Wicker JGPP, Cooper RI (2015) Will it crystallise? Predicting crystallinity of molecular
materials. CrystEngComm 17:1927–1934
205. Directed Assembly Network (2020) Directed assembly themes and streams. http://
directedassembly.org/themes-and-focus/
206. Grabowsky S, Genoni A, Bürgi H-B (2017) Quantum crystallography. Chem Sci 8:4159–4176
207. Lommerse JPM, Motherwell WDS, Ammon HL et al (2000) A test of crystal structure
prediction of small organic molecules. Acta Crystallogr Sect B Struct Sci 56:697–714
Leading Edge Chemical Crystallography Service Provision and Its Impact on. . .
139
adsorption and separation by computational screening of materials. Langmuir 28:14114–
14128
186. Barthel S, Alexandrov EV, Proserpio DM, Smit B (2018) Distinguishing metal–organic
frameworks. Cryst Growth Des 18:1738–1747
187. Miklitz M, Jelfs KE (2018) pywindow: automated structural analysis of molecular pores. J
Chem Inf Model 58:2387–2391
188. Coudert F-X, Fuchs AH (2016) Computational characterization and prediction of metal–
organic framework properties. Coord Chem Rev 307:211–236
189. Goldsmith J, Wong-Foy AG, Cafarella MJ, Siegel DJ (2013) Theoretical limits of hydrogen
storage in metal–organic frameworks: opportunities and trade-offs. Chem Mater 25:3373–
3382
190. Moghadam PZ, Islamoglu T, Goswami S, Exley J, Fantham M, Kaminski CF, Snurr RQ, Farha
OK, Fairen-Jimenez D (2018) Computer-aided discovery of a metal–organic framework with
superior oxygen uptake. Nat Commun 9:1378
191. Altintas C, Erucar I, Keskin S (2018) High-throughput computational screening of the metal
organic framework database for CH 4/H 2 separations. ACS Appl Mater Interf 10:3668–3679
192. Azar ANV, Velioglu S, Keskin S (2019) Large-scale computational screening of metal organic
framework (MOF) membranes and MOF-based polymer membranes for H 2 /N 2 separations.
ACS Sustain Chem Eng 7:9525–9536
193. Inokuma Y, Matsumura K, Yoshioka S, Fujita M (2017) Finding a new crystalline sponge
from a crystallographic database. Chem – An Asian J 12:208–211
194. Zhang L, Chen Z, Su J, Li J (2019) Data mining new energy materials from structure
databases. Renew Sust Energ Rev 107:554–567
195. Shi P-P, Tang Y-Y, Li P-F, Liao W-Q, Wang Z-X, Ye Q, Xiong R-G (2016) Symmetry
breaking in molecular ferroelectrics. Chem Soc Rev 45:3811–3827
196. Cole JM, Kreiling S (2002) Exploiting structure/property relationships in organic non-linear
optical materials: developing strategies to realize the potential of TCNQ derivatives.
CrystEngComm 4:232–238
197. Phan H, Hrudka JJ, Igimbayeva D, Lawson Daku LM, Shatruk M (2017) A simple approach
for predicting the spin state of homoleptic Fe(II) Tris-diimine complexes. J Am Chem Soc
139:6437–6447
198. Schober C, Reuter K, Oberhofer H (2016) Virtual screening for high carrier mobility in
organic semiconductors. J Phys Chem Lett 7:3973–3977
199. Kunkel C, Schober C, Oberhofer H, Reuter K (2019) Knowledge discovery through chemical
space networks: the case of organic electronics. J Mol Model 25:87
200. Cole JM, Low KS, Ozoe H, Stathi P, Kitamura C, Kurata H, Rudolf P, Kawase T (2014) Data
mining with molecular design rules identifies new class of dyes for dye-sensitised solar cells.
Phys Chem Chem Phys 16:26684–26690
201. Adalder TK, Dastidar P (2014) Crystal engineering approach toward selective formation of an
asymmetric supramolecular synthon in primary ammonium monocarboxylate (PAM) salts and
their gelation studies. Cryst Growth Des 14:2254–2262
202. Veits GK, Carter KK, Cox SJ, McNeil AJ (2016) Developing a gel-based sensor using crystal
morphology prediction. J Am Chem Soc 138:12228–12233
203. Elton DC, Boukouvalas Z, Butrico MS, Fuge MD, Chung PW (2018) Applying machine
learning techniques to predict the properties of energetic materials. Sci Rep 8:9059
204. Wicker JGPP, Cooper RI (2015) Will it crystallise? Predicting crystallinity of molecular
materials. CrystEngComm 17:1927–1934
205. Directed Assembly Network (2020) Directed assembly themes and streams. http://
directedassembly.org/themes-and-focus/
206. Grabowsky S, Genoni A, Bürgi H-B (2017) Quantum crystallography. Chem Sci 8:4159–4176
207. Lommerse JPM, Motherwell WDS, Ammon HL et al (2000) A test of crystal structure
prediction of small organic molecules. Acta Crystallogr Sect B Struct Sci 56:697–714
Leading Edge Chemical Crystallography Service Provision and Its Impact on. . .
139