Prsentation PowerPoint

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Inorganic structure prediction too much and not
enough Armel Le Bail Université du Maine,
Laboratoire des oxydes et Fluorures, CNRS UMR
6010, Avenue O. Messiaen, 72085 Le Mans Cedex 9,
France. Email alb_at_cristal.org
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• CONTENTS
• Introduction- Prediction software and
  examples- More examples from the GRINSP software
  (especially AlF3 polymorphs and
  titanosilicates)- Opened doors, limitations,
  problems- Conclusion

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INTRODUCTION
Personnal views about crystal structure
prediction Exact description before
synthesis or discovery in nature.
These exact descriptions should be used for the
calculation of powder patterns included in a
database for automatic identification of real
compounds not yet characterized
crystallographycally.
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Where are we with inorganic crystal structure
prediction?
If the state of the art had dramatically evolved
in the past ten years, we should have huge
databases of predicted compounds, and not any new
crystal structure would surprise us since it
would corespond already to an entry in that
database.
Moreover, we would have obtained in advance the
physical properties and we would have preferably
synthesized those interesting compounds.
Of course, this is absolutely not the case.
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But things are changing, maybe Two databases of
hypothetical compounds were built in 2004. One
is exclusively devoted to zeolites M.D. Foster
M.M.J. Treacy - Hypothetical Zeolites
http//www.hypotheticalzeolites.net/ The other
includes zeolites as well as other predicted
oxides (phosphates, borosilicates, etc) and
fluorides the PCOD (Predicted Crystallography
Open Database)http//www.crystallography.net/pcod
/
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Prediction software
Especially recommended lectures (review papers)
1- S.M. Woodley, in Application of
Evolutionary Computation in Chemistry, R. L.
Johnston (ed), Structure and bonding series,
Springer-Verlag 110 (2004) 95-132. 2- J.C. Schön
M. Jansen, Z. Krist. 216 (2001) 307-325
361-383. Software CASTEP, program for
Zeolites, GULP, G42, Spuds, AASBU, GRINSP
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CASTEP Uses the density functional theory (DFT)
for ab initio modeling, applying a
pseudopotential plane-wave code. M.C Payne et
al., Rev. Mod. Phys. 64 (1992) 1045. Example
carbon polymorphs
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HypotheticalCarbonPolymorphSuggestedByCASTEP
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Another CASTEP prediction
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ZEOLITES The structures gathered in the database
of hypothetical zeolites are produced from a
64-processor computer cluster grinding away
non-stop, generating graphs and annealing them,
the selected frameworks being then re-optimized
using the General Utility Lattice Program (GULP,
written by Julian Gale) using atomic potentials.
M.D. Foster M.M.J. Treacy - Hypothetical
Zeolites http//www.hypotheticalzeolites.net/
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Zeolite predictions are probably too much
Less than 200 zeotypes are known Less than 10 new
zeotypes are discovered every year Less than half
of them are listed in that gt1.000.000 database So
that zeolite predictions will continue up to
attain several millions more Quantum chemistry
validation of these prediction is required, not
only empirical energy calculations, for
elimination of a large number of models that will
certainly never be confirmed.
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GULP Appears to be able to predict crystal
structures (one can find in the manual the data
for the prediction of TiO2 polymorphs).
Recently, a genetic algorithm was implemented
in GULP in order to generate crystal framework
structures from the knowledge of only the unit
cell dimensions and constituent atoms (so, this
is not prediction...), the structures of the
better candidates produced are relaxed by
minimizing the lattice energy, which is based on
the Born model of a solid. S.M. Woodley, in
Application of Evolutionary Computation in
Chemistry, R. L. Johnston (ed), Structure and
bonding series, Springer-Verlag 110 (2004)
95-132. GULP J. D. Gale, J. Chem. Soc., Faraday
Trans., 93 (1997) 629-637. http//gulp.curtin.edu.
au/
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Part of the command list of GULP
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G42 A concept of 'energy landscape' of chemical
systems is used by Schön and Jansen for structure
prediction with their program named G42. J.C.
Schön M. Jansen, Z. Krist. 216 (2001) 307-325
361-383.
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SPuDS Dedicated especially to the prediction of
perovskites. M.W. Lufaso P.M. Woodward, Acta
Cryst. B57 (2001) 725-738.
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AASBU method (Automated Assembly of Secondary
Building Units) Developed by Mellot-Draznieks
et al., C. Mellot-Drazniek, J.M. Newsam, A.M.
Gorman, C.M. Freeman G. Férey, Angew. Chem.
Int. Ed. 39 (2000) 2270-2275 C.
Mellot-Drazniek, S. Girard, G. Férey, C. Schön,
Z. Cancarevic, M. Jansen, Chem. Eur. J. 8 (2002)
4103-4113. Using Cerius2 and GULP in a sequence
of simulated annealing plus minimization steps
for the aggregation of large structural
motifs. Cerius2, Version 4.2, Molecular
Simulations Inc., Cambridge, UK, 2000.
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Not enough If zeolites are excluded, the
productions of these prediction software are a
few dozen not enough, not available in any
database. A recent (2005) prediction program is
able to extendthe investigations to larger
series of inorganic compounds characterized by
corner-sharing polyhedra.
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GRINSP Geometrically Restrained INorganic
Structure Prediction Applies the knowledge about
the geometrical characteristics of a particular
group of inorganic crystal structures
(N-connected 3D networks with N 3, 4, 5, 6,
for one or two N values). Explores that limited
and special space (exclusive corner-sharing
polyhedra) by a Monte Carlo approach. The cost
function is very basic, depending on weighted
differences between ideal and calculated
interatomic distances for first neighbours M-X,
X-X and M-M for binary MaXb or ternary MaM'bXc
compounds.
J. Appl. Cryst. 38, 2005, 389-395. J. Solid State
Chem. 179, 2006, 3159-3166.
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Observed and predicted cell parameters comparison
Predicted by GRINSP (Å) Observed or
idealized (Å) Dense SiO2 a b c R a b
c ??? () Quartz 4.965 4.965 5.375 0.0009
4.912 4.912 5.404 0.9Tridymite 5.073 5.07
3 8.400 0.0045 5.052 5.052 8.270  0.8Crist
obalite 5.024 5.024 6.796 0.0018 4.969 4.96
9 6.926 1.4 Zeolites ABW 9.872 5.229 8.733
0.0056 9.9 5.3 8.8 0.8EAB 13.158 13.
158 15.034 0.0037 13.2 13.2 15.0 0.3EDI 6
.919 6.919 6.407 0.0047 6.926 6.926 6.410
0.1GIS 9.772 9.772 10.174 0.0027 9.8 9.8
10.2 0.3GME 13.609 13.609 9.931 0.0031
13.7 13.7 9.9 0.6 Aluminum
fluorides?-AlF3 10.216 10.216 7.241 0.0159 1
0.184 10.184 7.174  0.5Na4Ca4Al7F33 10.876 10.87
6 10.876 0.0122 10.781 10.781 10.781 0.9AlF3-py
rochl. 9.668 9.668 9.668 0.0047 9.749 9.749
9.749 0.8 TitanosilicatesBatisite 10.633 14.0
05 7.730 0.0076 10.4 13.85 8.1 2.6Pabsti
te 6.724 6.724 9.783 0.0052 6.7037 6.7037 9
.824 0.9Penkvilskite 8.890 8.426 7.469 0.007
6 8.956 8.727 7.387 1.3
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Predictions produced by GRINSP Binary
compounds Formulations M2X3, MX2, M2X5 et MX3
were examined. Zeolites MX2 ( 4-connected 3D
nets) More than 1000 zeolites (not 1.000.000) are
proposed with cell parameters lt 16 Å, placed into
the PCOD database http//www.crystallography.net
/pcod/ GRINSP recognizes a zeotype by comparing
the coordination sequences (CS) of a model with a
previously established list of CS and with the CS
of the models already proposed during the current
calculation).
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Hypothetical zeolite PCOD1010026SG P432, a
14.623 Å, FD 11.51
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Other GRINSP predictions gt 3000 B2O3
polymorphs
Hypothetical B2O3 - PCOD1062004.Triangles BO3
sharing corners. 3-connected 3D nets
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gt 500 V2O5 polymorphs
square-based pyramids 5-connected 3D nets
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12 AlF3 polymorphs
Corner-sharing octahedra. 6-connected 3D nets
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Do these AlF3 polymorphs can really exist ?
Ab initio energy calculations by WIEN2K  Full
Potential (Linearized) Augmented Plane Wave
code 
A. Le Bail F. Calvayrac, J. Solid State Chem.
179 (2006) 3159-3166.
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Ternary compounds MaMbXc in 3D networks of
polyhedra connected by corners Either M/M with
same coordination but different ionic radii or
with different coordinations (mixed
N-N-connected 3D frameworks) These ternary
compounds are not always electrically neutral.
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Borosilicates PCOD2050102, Si5B2O13, R 0.0055.
SiO4 tetrahedraandBO3 triangles
gt 3000 models
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Aluminoborates
Example AlB4O9-2, cubic, SG Pn-3, a 15.31
Å, R 0.0051
AlO6 octahedra andBO3triangles
gt 2000 models
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Fluoroaluminates Known Na4Ca4Al7F33 PCOD1000015
- Ca4Al7F334-.
Two-sizesoctahedra AlF6 and CaF6
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Unknown PCOD1010005 - Ca3Al4F213-
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Results for titanosilicates
TiO6 octahedra andSiO4 tetrahedra
gt 1000 models
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More than 70 of the predicted titanosilicates
have the general formula TiSinO(32n)2-
Numbers of compounds in ICSD version 1-4-1,
2005-2 (89369 entries) potentially fitting
structurally with the TiSinO(32n)2- series of
GRINSP predictions, addingeither C, C2 or CD
cations for electrical neutrality.
n C C2 CD Total GRINSP ABX5
1 300 495 464 35 1294 93AB2X7
2 215 308 236 11 770 179AB3X9
3 119 60 199 5 383 174AB4X11
4 30 1 40 1 72 205AB5X13 5 9 1 1 0 11 36AB6
X15 6 27 1 13 1 42 158Total 2581 845
Not all these 2581 ICSD structures are built up
from corner sharing octahedra and tetrahedra.
Many isostructural compounds inside.
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Models with real counterparts
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Example in PCOD
Model PCOD2200207 (Si3TiO9)2- a 7.22 Å b
9.97 Å c 12.93 Å, SG P212121
Known as K2TiSi3O9.H2O (isostructural to mineral
umbite)a 7.1362 Å b 9.9084 Å c 12.9414
Å, SG P212121(Eur. J. Solid State Inorg. Chem.
34, 1997, 381-390)
Not too bad if one considers that K et H2O are
not taken into account in the model prediction...
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Highest quality (?) models
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Models with the largest porosity
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PCOD3200086 P 70.2, FD 10.6, DP 3
(dimensionality of the pore/channels system)
Ring apertures9 x 9 x 9
Si6TiO152- , cubic, SG P4132, a 13.83 Å
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PCOD3200867, P 61.7, FD 12.0, DP 3
Si2TiO72- , orthorhombic, SG Imma
Ring apertures10 x 8 x 8
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PCOD3200081, P 61.8, FD 13.0, DP 3
Si6TiO152- , cubic, SG Pn-3
Ring apertures12 x 12 x 12106
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PCOD3200026, P 59.6, FD 13.0, DP 3
Si4TiO112- , tetragonal, SG P42/mcm
Ring apertures12 x 10 x 10
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Opened doors, Limitations, Problems GRINSP
limitation exclusively corner-sharing
polyhedra. Opening the door potentially to gt
50.000 hypothetical compounds.The predicted
titanosilicates can be extrapolated to
phosphates, sulfates, and/or replacing Ti by Nb,
V, Zr, Ga, etc. More than 10.000 should be
included into PCOD before the end of
2006. Then, their powder patterns will be
calculated and possibly used for search-match
identification.
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Expected improvements Edge, face,
corner-sharing, mixed. Hole detection, filling
them automatically, appropriately, for
electrical neutrality. Using bond valence rules
or/and energy calculationsto define a new cost
function. Extension to quaternary compounds,
combining more than two different
polyhedra. Etc, etc. Do it yourself, the GRINSP
software is open source
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Two things that dont work well enough up to now
Validation - Ab initio calculations (WIEN2K, etc)
not fast enough for the validation of gt 10000
structure candidates
(was 2 months for 12 AlF3 models)
Identification - There is no efficient tool for
the identification of the known structures (from
the ICSD) among gt10000 hypothetical compounds
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One advice, if you become a structure predictor
Send your data (CIFs) to the PCOD, thanks(no
proteins, no nucleic acid, not 1.000.000 zeolites)
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CONCLUSIONS Structure and properties prediction
is THE challenge of this XXIth century in
crystallography. Advantages are obvious (less
serendipity and fishing-type syntheses). We have
to establish databases of predicted compounds,
preferably open access on the Internet,finding
some equilibrium between too much and not
enough. If we are unable to do that, we have to
stop pretending to understand and master the
crystallography laws.