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Title: Prsentation PowerPoint

Frontiers Between Crystal Structure Prediction
and Determination by Powder Diffractometry Armel
Le Bail Université du Maine, Laboratoire des
Oxydes et Fluorures, CNRS UMR 6010, Avenue O.
Messiaen, 72085 Le Mans, France Email
  • Outline
  • Introduction - Prediction software and
    examples - Fuzzy frontiers with SDPD - More
    examples from the GRINSP software
    - Opened doors, limitations, problems -
     Immediate structure solution  by
    search-match - Conclusion - Live demo with
    EVA-Bruker PPDF-1

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 actual
compounds not yet characterized
Where are we with inorganic crystal structure
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
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.
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
The other includes zeolites as well as other
predicted oxides (phosphates, sulfates,
silicates, borosilicates, etc) and fluorides
the PCOD (Predicted Crystallography Open
Database) http//
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
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
Hypothetical Carbon Polymorph Suggested By CASTEP
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//
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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.
GULP (at the Frontier ?) 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 full 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.
Part of the command list of GULP
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
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SPuDS Dedicated especially to the prediction of
perovskites. M.W. Lufaso P.M. Woodward, Acta
Cryst. B57 (2001) 725-738.
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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  • Two (incredible ?) predictions
  •  Giant structures solved by
  • combined targeted chemistry and
    computational design." 2 cubic hybrid solids
    structures published
  • - 2004 - V 380.000 Å3, a 73Å, Fd-3m, 68
    indpdt atoms (not-H)
  • 2005 - V 702.000 Å3, a 89Å, Fd-3m, 74 indpdt
    atoms (not-H)
  • Presented more or less as being predicted, but
    with indexed powder patterns and guessed content.
  • Are they SDPD or predictions ? The fuzzy frontier
    is there…

Angew. Chem. Int. Ed. 43 (2004) 2-7.
Super-tetrahedra sharing corners, building
super-zeolites (MTN-analogue)
Science 309 (2005) 2040-2042.
Same  prediction  process, building another
MTN-analogue super-zeolite
From different super-tetrahedra
Will you be able to equal or surpass these
giant structure  predictions  ?
YES If the molecule, the cell and the space group
are known, then the direct space methods need
only 50 or 100 reflections for solving the
structure, whatever the cell volume (6 DoF per
molecule rotated and translated). But this is not
prediction. MAYBE By partial prediction (without
cell but with known content). This is  molecular
packing prediction . NO Without cell and without
content, full total prediction at such complexity
level looks impossible. Anyway, you may try to
impress some Nature or Science reviewer searching
for  sensational  results, by your eloquence.
Not enough full predictions If zeolites are
excluded, the productions of these prediction
software are a few dozen… not enough, and not
available in any database. The recent (2005)
prediction program GRINSP is able to extend the
investigations to larger series of inorganic
compounds characterized by corner-sharing
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
J. Appl. Cryst. 38, 2005, 389-395. J. Solid State
Chem. 179, 2006, 3159-3166.
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 0.9 Tridymite 5.073 5.07
3 8.400 0.0045 5.052 5.052 8.270  0.8 Crist
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 0.8 EAB 13.158 13.
158 15.034 0.0037 13.2 13.2 15.0 0.3 EDI 6
.919 6.919 6.407 0.0047 6.926 6.926 6.410
0.1 GIS 9.772 9.772 10.174 0.0027 9.8 9.8
10.2 0.3 GME 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  0.5 Na4Ca4Al7F33 10.876 10.87
6 10.876 0.0122 10.781 10.781 10.781 0.9 AlF3-py
rochl. 9.668 9.668 9.668 0.0047 9.749 9.749
9.749 0.8 Titanosilicates Batisite 10.633 14.0
05 7.730 0.0076 10.4 13.85 8.1 2.6 Pabsti
te 6.724 6.724 9.783 0.0052 6.7037 6.7037 9
.824 0.9 Penkvilskite 8.890 8.426 7.469 0.007
6 8.956 8.727 7.387 1.3
Predictions produced by GRINSP Binary
compounds Formulations M2X3, MX2, M2X5 et MX3
were examined. Zeolites MX2 ( 4-connected 3D
nets) More than 4700 zeolites (not 1.000.000) are
proposed with cell parameters lt 16 Å, placed into
the PCOD database http//
/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
Hypothetical zeolite PCOD1010026 SG P432, a
14.623 Å, FD 11.51
Other GRINSP predictions gt 3000 B2O3
Hypothetical B2O3 - PCOD1062004. Triangles BO3
sharing corners. 3-connected 3D nets
gt 1300 V2O5 polymorphs
square-based pyramids 5-connected 3D nets
gt30 AlF3 polymorphs
Corner-sharing octahedra. 6-connected 3D nets
Do these AlF3 polymorphs can really exist ?
Ab initio energy calculations by WIEN2K  Full
Potential (Linearized) Augmented Plane Wave
A. Le Bail F. Calvayrac, J. Solid State Chem.
179 (2006) 3159-3166.
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.
Borosilicates PCOD2050102, Si5B2O13, R 0.0055.
SiO4 tetrahedraand BO3 triangles
gt 3000 models
Example AlB4O9-2, cubic, SG Pn-3, a 15.31
Å, R 0.0051
AlO6 octahedra and BO3 triangles
gt 4000 models
Fluoroaluminates Known Na4Ca4Al7F33 PCOD1000015
- Ca4Al7F334-.
Two-sizes octahedra AlF6 and CaF6
Unknown PCOD1010005 - Ca3Al4F213-
Results for titanosilicates
TiO6 octahedra and SiO4 tetrahedra
gt 1700 models
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, adding either C, C2 or CD
cations for electrical neutrality.
1 300 495 464 35 1294 130 TiSiO5 AB2X7
2 215 308 236 11 770 207 TiSi2O7 AB3X9
3 119 60 199 5 383 215 TiSi3O9 AB4X11
4 30 1 40 1 72 257 TiSi4O11 AB5X13
5 9 1 1 0 11 75 TiSi5O13 AB6X15
6 27 1 13 1 42 207 TiSi6O15 Total 2581 1091
Not all these 2581 ICSD structures are built up
from corner sharing octahedra and tetrahedra.
Many isostructural compounds inside.
Models with real counterparts
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...
Highest quality (?) models
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Models with the largest porosity
PCOD3200086 P 70.2, FD 10.6, DP 3
(dimensionality of the pore/channels system)
Ring apertures 9 x 9 x 9
Si6TiO152- , cubic, SG P4132, a 13.83 Å
PCOD3200867, P 61.7, FD 12.0, DP 3
Si2TiO72- , orthorhombic, SG Imma
Ring apertures 10 x 8 x 8
PCOD3200081, P 61.8, FD 13.0, DP 3
Si6TiO152- , cubic, SG Pn-3
Ring apertures 12 x 12 x 12 106
PCOD3200026, P 59.6, FD 13.0, DP 3
Si4TiO112- , tetragonal, SG P42/mcm
Ring apertures 12 x 10 x 10
Opened doors, Limitations, Problems GRINSP
limitation exclusively corner-sharing
polyhedra. Opening the door potentially to gt
1.000.000 hypothetical compounds. More than
60.000 silicates, phosphates, sulfates of Al, Ti,
V, Ga, Nb, Zr, or zeolites, fluorides, etc. were
included into PCOD in february 2007. Their
powder patterns were calculated, building the
PPDF-1 (Predicted Powder Diffraction File version
1) for search-match identification.
Predicted crystal structures provide predicted
Calculated powder patterns in the PPDF-1 allow
for identification by search-match (EVA - Bruker
and Highscore - Panalytical) Providing a way for
 immediate structure solution  We  simply 
need for a complete database of predicted
structures -)
Example 1 The actual and virtual structures
have the same chemical formula, PAD 0.52
(percentage of absolute difference on cell
parameters, averaged) ?-AlF3, tetragonal, a
10.184 Å, c 7.174 Å. Predicted  10.216 Å,
7.241 Å. A global search (no chemical restraint)
is resulting in the actual compound (PDF-2) in
first position and the virtual one (PPDF-1) in
2nd (green mark in the toolbox).
Example 2 Model showing uncomplete chemistry,
PAD 0.63. Actual compound K2TiSi3O9?H2O,
orthorhombic, a 7.136 Å, b 9.908 Å, c 12.941
Å. Predicted framework TiSi3O9, a 7.22 Å, b
9.97 Å, c 12.93 Å. Without chemical restraint,
the correct PDF-2 entry is coming at the head of
the list, but no virtual model. By using the
chemical restraint (Ti Si O), the correct
PPDF-1 entry comes in second position in spite of
large intensity disagreements with the
experimental powder pattern (K and H2O are
lacking in the PCOD model) 
Example 3 Model showing uncomplete chemistry,
PAD 0.88. Predicted framework Ca4Al7F33,
cubic, a 10.876 Å. Actual compound
Na4Ca4Al7F33, a 10.781 Å. By a search with
chemical restraints (Ca Al F) the virtual
model comes in fifth position, after 4 PDF-2
correct entries, if the maximum angle is limited
to 30(2?)
Example 4 heulandite
Example 5 Mordenite
Two main problems in identification by
search-match process from the PPDF-1 -
Inaccuracies in the predicted cell parameters,
introducing discrepancies in the peak
positions. - Uncomplete chemistry of the models,
influencing the peak intensities. However,
identification may succeed satisfyingly if the
chemistry is restrained adequately during the
search and if the averaged difference in cell
parameters is smaller than 1.
A similarity index less sensitive to cell
parameter discrepancies
 New similarity index for crystal structure
determination from X-ray powder diagrams, 
D.W.M. Hofmann and L. Kuleshova, J. Appl.
Cryst. 38 (2005) 861-866.
Typical case to be solved by prediction
d-Zn2P2O7 Bataille et al., J. Solid State Chem.
140 (1998) 62-70.
Uncertain indexing, line profiles broadened by
size/microstrain effects (Powder pattern not
better from synchrotron radiation than from
conventional X-rays)
But the fingerprint is there…
Expected GRINSP improvements Edge, face,
corner-sharing, mixed. Hole detection, filling
them automatically, appropriately, for
electrical neutrality. Using bond valence rules
or/and energy calculations to 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… Nothing planned about
Current PCOD Content
4786 SiO2 the isostructural (Al/P)O4, (Al/Si)O4
and (Al/S)O4 4138 AlO6/BO3 2394 VO5/PO4 the
isostructural VO5/SiO4, VO5/SO4,
TiO5/SiO4 1747 TiO6/SiO4 the isostructural
phosphates and sulfates
and also replacing Ti by Ga, Nb, V,
Zr 1328 TiO6/VO5 the isostructural
VO6/VO5 1318 V2O5 33 AlF3 the isostructural
FeF3, GaF3 and CrF3 24 AlF6/CaF6 13 AlF6/NaF6 15.7
81 different structure-types, gt 60.000
hypothetical phases You may ask for other
isostructural series or build them… Expected gt
120.000 at the next update in September 2007…
Two things that dont work well enough up to now…
Validation of the Predictions - Ab initio
calculations (WIEN2K, etc) not fast enough for
the validation of gt 60000 structure candidates
(was 2 months for 12
AlF3 models)
Identification (is this predicted structure
already known?) - There is no efficient tool for
the fast comparison of these thousands of
inorganic predicted structures to the known
structures (inside of ICSD)
One advice, if you become a structure predictor
Send your data (CIFs) to the PCOD,
thanks… http//
CONCLUSIONS Structure and properties full
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