computer crystallochemical analysis: an...
TRANSCRIPT
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Computer crystallochemical analysis: an overview
Vladislav A. Blatov Samara State University, Samara, Russia
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Main ideas
• We are at a new stage of crystal chemistry development• Computers can do much more in crystal chemistry than you suppose• Crystallographic data contain much more information than you think
Crystal chemistry => chemical composition vs. crystal structure
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Content
1. Why listen to this lecture? 2. Why crystallochemical analysis?
3. Why computer crystallochemical analysis? 4. History of crystallochemical analysis
5. General principles of computer crystallochemical analysis 6. Models
7. Data 8. Algorithms
9. Results 10. Outlook
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Why listen to this lecture?Diamond
Platon CrystalMaker
…
Do you know any general crystallochemical
law?
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What should crystallochemical analysis do?
crystallochemical = stereochemical
=> local parameters
crystallochemical => global parameters
(i)
analysis of chemical interactions in crystals crystallochemical analysis
(ii)analysis of the crystal architecture built by
chemical interactions
crystallochemical
analysis
Crystal chemistry => chemical composition vs. crystal structure
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Why computer crystallochemical analysis?
CSD 500000 entries
ICSD 100000 entries
CrystMet 100000 entries
PCD 180000 entries
Crystallochemical laws / regularities
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Why computer crystallochemical analysis?
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Why computer crystallochemical analysis?
Subjectivity Objectivity
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Content
1. Why listen to this lecture? 2. Why crystallochemical analysis?
3. Why computer crystallochemical analysis? 4. History of crystallochemical analysis
5. General principles of computer crystallochemical analysis 6. Models
7. Data 8. Algorithms
9. Results 10. Outlook
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History of crystallochemical analysis
Observational phenomenological
stage
Kepler's
tract “The six-cornered snowflake", 1611
Steno’s law of constancy of dihedral angles, 1669
Haüy’s
law of rational relations, 1784
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History of crystallochemical analysis
Observational phenomenological
stage
E. Mitscherlich
–
isomorphism, 1819
E. Mitscherlich
–
polymorphism, 1822
Crystallochemical
phenomena!
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History of crystallochemical analysis
Mathematical fundamentals
J. Hessel, 1830 –
32 geometric crystal classes
A. Bravais, 1848 –
14 three-periodic lattices
E. Fedorov
& A. Schönflies, 1890 –
230 space groups
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History of crystallochemical analysis
Microscopic observations
M. Laue, 1912 –
diffraction of X-rays in crystals
W.G. Bragg and W.L. Bragg, 1913 –
first structure
determinations
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History of crystallochemical analysis
Experimental technique and methods of X-ray analysis
1920s –
1960sPhotomethods
and technique
First printed manuals on crystal structuresFirst really crystallochemical laws –(L. Pauling, V. Goldschmidt, A. Kitaigorodskii)A.F. Wells, 1954 –
graph representation
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History of crystallochemical analysis
Time of automated diffractometers
1960s –
present time
Rapid accumulation of experimental data
Now the number of determined crystal structures exceeds 600,000 and grows faster and faster
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History of crystallochemical analysis
Time of electronic crystal structure databases
1970s –
present time
Death of printed handbooks (Structurbericht,
Structure Reports)
Appearance of CSD, ICSD, CrystMet, PDB
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History of crystallochemical analysis
Creating mathematical methods, computer algorithms and programs for automated crystallochemical
analysis of the electronic crystal structure databases
1990s –
present timeApplications:
crystal design, crystal engineering, nanochemistry, supramolecular
chemistry and other modern trends in materials science
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History of crystallochemical analysis
General crystallochemical laws "composition –
structure –
property"
2010s (?) –
future time
Applications: creating new crystals and materials
The discoverers are sitting here (?)
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Content
1. Why listen to this lecture? 2. Why crystallochemical analysis?
3. Why computer crystallochemical analysis? 4. History of crystallochemical analysis
5. General principles of computer crystallochemical analysis 6. Models
7. Data 8. Algorithms
9. Results 10. Outlook
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General principles of computer crystallochemical analysis
1.
Use only reliable structure data (cell dimensions, space group, atomic coordinates). Be careful with modeling data!
2.
Only structure data are the basis for mathematical models. The models should be quite simple and universal to process structures of different composition and complexity.
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General principles of computer crystallochemical analysis
3.
All basic concepts of the models must be strictly defined. The concepts have to be clear to computer that has much more formal logic than a man!
4.
The data analysis must be formalized as well.
5.
The crystallochemical computer programs have to be able to analyze the whole available experimental information.
6.
No information should be ignored!
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Content
1. Why listen to this lecture? 2. Why crystallochemical analysis?
3. Why computer crystallochemical analysis? 4. History of crystallochemical analysis
5. General principles of computer crystallochemical analysis 6. Models
7. Data 8. Algorithms
9. Results 10. Outlook
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Models: Lattice
Hexagonal lattice Atom coordinates C1 0.00000 0.00000 0.00000 Space Group: P6/mmm Cell Dimensions a=1.0000 b=1.0000 c=10.0000 =90.00 =90.00 =120.00
Crystallographic, not crystallochemical model
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Models: Net
Inherently crystallochemical, but no geometrical properties are analyzed
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Models: Labeled quotient graph
Chung, S.J., Hahn, Th. & Klee, W.E. (1984). Acta Cryst. A40, 42-50.
h.c.p. 3D graphVertices & edges of the labeled quotient graph
Labeled quotient graph
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Models: Embedded net
Diamond (dia) net in the most symmetrical embedding
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Models: Polyhedral subdivision
Voronoi-Dirichlet polyhedron and partition: bcu
net
Kd
=0.5
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Models: Polyhedral subdivision
Physical meaning of Voronoi-Dirichlet polyhedra
Parameter Meaning
Volume, Å3 Relative size of atom or molecule in the crystal field
Rsd , ÅGeneralized crystallochemical atomic/molecular radius
i Strength of atomic/molecular interaction
Nf Number of atoms/molecules in contact
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Models: Polyhedral subdivision
Tilings: dia
and bcu
nets
dia bcu
‘Normal’
crystal chemistry ‘dual’
crystal chemistry
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Content
1. Why listen to this lecture? 2. Why crystallochemical analysis?
3. Why computer crystallochemical analysis? 4. History of crystallochemical analysis
5. General principles of computer crystallochemical analysis 6. Models
7. Data 8. Algorithms
9. Results 10. Outlook
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Data: Electronic crystallographic databases
CSD 500000 entries
ICSD 100000 entries
CrystMet 100000 entries
Pearson’s Crystal Data
180000 entries
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Data: Electronic crystallochemical databases
RCSR 1764
entries; http://rcsr.anu.edu.au
TTD Collection 71067 entries; http://www.topos.ssu.samara.ru
TTO Collection 9006
entries; http://www.topos.ssu.samara.ru
Atlas of Zeolite Frameworks, 194
entries; http://www.iza-structure.org/databases/
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Data: Electronic databases of hypothetical nets
EPINET 14532 entries; http://epinet.anu.edu.au/
Atlas of Prospective Zeolite Frameworks 5,389,407
entries;
http://www.hypotheticalzeolites.net/
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Content
1. Why listen to this lecture? 2. Why crystallochemical analysis?
3. Why computer crystallochemical analysis? 4. History of crystallochemical analysis
5. General principles of computer crystallochemical analysis 6. Models
7. Data 8. Algorithms
9. Results 10. Outlook
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Algorithms: requirements
universality rigour
automation
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Algorithms: Searching for errors in crystal data
Parameters to be checked:
-
interatomic distances, bond or dihedral angles - VDP volume
-
VDP second moment of inertia -
atomic coordination number
-
dimensionality of complex groups
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Algorithms: building adjacency matrix
Method of intersecting spheres
For inorganic compounds
Method of spherical sectors
For organic, inorganic and metal-organic compounds
Distances For all types of compounds, using atomic radii and Voronoi polyhedra
Solid Angles For artificial nets, intermetallides, noble gases, using Voronoi polyhedra
Van der
Waals Specific Valence Valence Valence
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Algorithms: building adjacency matrix
Solid angle of a VDP face is proportional to the bond strength
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Algorithms: building Voronoi polyhedra
Very fast 'gift wrapping' algorithm (Preparata
& Shamos, 1985) of computing a convex hull for a set of image points with coordinates (2xi
/Ri2, 2yi
/Ri2, 2zi
/Ri2),
where (xi
, yi
, zi
) are the Cartesian coordinates of the surrounding atoms, and Ri
is the distance from the VDP atom to the ith
neighboring atom.
Initial structure Image points Convex hull Structure & VDP
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Algorithms: structure representations
Complete representation
(all bonds, all atoms)
Partial representations(not all bonds)
Partial representations(not all atoms)
Partial representations(not all bonds, not all atoms)
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Algorithms: structure representations
complete representation partial representation
benzene
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Algorithms: structure representations
complete representation partial representations
Ice VIII
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Algorithms: structure representations
Diaqua-bis(glycolato)-Mn(II) Complete representation
Standard partial representation
Standard simplified representation (vcn; 7/3/o11)
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Algorithms: structure representations
2-Butyne-1,4-diol Complete representation
Standard partial representation
Standard simplified representation (dia)
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Valence bonds
Van der Waals bonds
Hydrogen bonds
Weights
Algorithms: structure representations
Weighted colored labeled quotient graph
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Algorithms: structure representations
Clustering bonds in -CaSO4
> 10.5% sma
> 14.0% qtz
> 24.5%
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I II
Atom roles: origin (OA), removed (RA), contracted (CA), target (TA)
Contracted atom Target atoms Origin: U, Cu, P; Removed: water molecules Contracted: phosphate oxygens
Target: P
Cu[UO2
PO4
]2
12H2
O
Algorithms: structure representations
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Algorithms: structure representations
(i) {All atoms}={Atoms}+{Pseudo-atoms} (ii) {OA}{RA}=; {OA}{CA}=, {RA}{CA}=
(iii) {TA}{OA} (iv) {OA}{RA}{CA}={All atoms}
(v) {OA} (vi) {TA}
{CA}
{OA}+{RA}+{CA}+{TA} = collection = structure representation
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Algorithms: structure representations
{A, B}={All atoms} Collections ({OA},{RA},{CA},{TA}):
(i) ({A}, {B}, , ), the subnet of A atoms; (ii) ({A}, , {B}, {A}), the net of A atoms with the A–B–A
bridges (B atoms are spacers); (iii) ({B}, {A}, , ); (iv) ({B}, , {A}, {B}) are the same
nets of B atoms. There are four representations of this structure at a
given level of bond strength.
Example: a binary compound An
Bm
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1. Coordination sequence (CS)
2. Extended point
symbol (ES)
3. Vertex symbol (VS)
Determining net topology
Ni: 6 20 42 74 114 164 222 290 366 452As: 6 18 42 74 114 162 222 290 366 450Ni: 4.4.4.4.4.4.4.4.4.4.4.4.64
.64
.64
As: 4.4.4.4.4.4.42
.42
.42
.68
.68
.68
.68
.68
.68
Ni: 4.4.4.4.4.4.4.4.4.4.4.4.*.*.*As: 4.4.4.4.4.4.42
.42
.42
.64
.64
.64
.64
.64
.64
NiAs
Topological type: nia
RCSR 1764 entries http://rcsr.anu.edu.au/
EPINET 14532 entrieshttp://epinet.anu.edu.au/
TTD Collection 71067 entrieshttp://www.topos.ssu.samara.ru
TOPOS
ADS program
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Shell graph SGn (N); n shells, N atoms + origin, N=Ni ; i=1-n
SG1 (4) SG2 (16) SG3 (40)
SG4 (82) SG5 (146)
Diamond, dia
Degree of similarity D=n:N
Algorithms: determining net topology
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Algorithms: ambient isotopic nets
Polythreading, 1D+1D1D Polycatenation, 2D+2D3D
Interpenetration, 3D+3D3D Self-catenation
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Algorithms: link types
Hopf non-Hopf Multiknot Threading
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Algorithms: tilings
Weak ring
Catenated
rings Crossing rings
Essential rings
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Algorithms: tilings
Zeolite IMF, 19 kinds of tiles, 16 topological types
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Shell graph SGn (N); n shells, N atoms + origin, N=Ni ; i=1-n
SG1 (4) SG2 (16) SG3 (40)
SG4 (82) SG5 (146)
Diamond, dia
Algorithms: nanoclusters 1. Primary nanoclusters
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Algorithms: nanoclusters 2. Highest symmetry and homogeneity
Zn4, 96gZn3, 48fZn1, 16c Zn2, 16d
Zr, 8b
ZrZn22
Samson S. (1961) Acta Cryst. 14,
1229
Ilyushin G.D., Blatov V.A. (2009)
Acta Cryst. B65, 300
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Algorithms: nanoclusters 3. Highest coordination number
Zn4, 12Zn3, 12Zn1, 12 Zn2, 14
Zr, 16
ZrZn22
Samson S. (1961) Acta Cryst. 14,
1229
Ilyushin G.D., Blatov V.A. (2009)
Acta Cryst. B65, 300
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Algorithms: nanoclusters 4. Nanoclusters do not interpenetrate
β-Mg2
Al3
: five fused 61-atom 2-shell nanoclusters
centers 1st
shell 2nd
shell
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β-Mg2
Al3
: complete constructing with two kinds of 2-shell nanoclusters
Algorithms: nanoclusters 5. Nanoclusters include all atoms of the structure
centers 1st
shell 2nd
shell
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Algorithms: nanoclusters 6. Parsimony (Ockham’s Razor) principle
β-Mg2
Al3
: 44 possible nanocluster models
Mg9(2) Al14(2)Mg9(1) Al14(2) Mg3(1) ZA1(8a)(3) Mg9(1) ZA2(16d)(2)ZA1(8a)(3) Mg9(1) ZA2(16d)(1)Mg9(2) ZA2(16d)(1) Al14(2)ZA1(8a)(1) Mg9(2) Al14(2)…
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Algorithms: nanoclusters 7. Primary nanoclusters and supraclusters
β-Mg2
Al3
: supracluster AB2
consisting of three primary nanoclusters
61-atom nanocluster A
63-atom nanocluster B
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Algorithms: nanoclusters 8. Underlying net
β-Mg2
Al3
: underlying net AB2
of the topology of the Laves phase MgCu2
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Algorithms: nanoclusters 9. Self-assembly
Assembling supraclusters
to the Laves phase MgCu2
network
β-Mg2
Al3
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Content
1. Why listen to this lecture? 2. Why crystallochemical analysis?
3. Why computer crystallochemical analysis? 4. History of crystallochemical analysis
5. General principles of computer crystallochemical analysis 6. Models
7. Data 8. Algorithms
9. Results 10. Outlook
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dia 41.9%
srs 7.6%
pcu 17.3%others
33.2%
Blatov, V. A., Carlucci, L., Ciani, G. &
Proserpio, D. M. (2004).
CrystEngComm, 6, 377-395.
301 Interpenetrating MOFs
Baburin, I. A., Blatov, V. A., Carlucci, L.,
Ciani, G. & Proserpio, D. M. (2005).
J. Solid State Chem. 178, 2452-2474.
144 Interpenetrating inorganic frameworks
dia 44.4%
srs 6.3%
pcu 18.8%
others 30.5%
Results: topological classification of nets
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dia 20.7%
srs 6.4%
pcu 12.9%
others 60.0%
Ockwig, N. W., Delgado-Friedrichs, O., O'Keeffe, M. & Yaghi, O. M. (2005). Acc. Chem. Res. 38, 176-182.
1127 metal-organic frameworks
Uninodal Edge-transitive
Results: topological classification of nets
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Baburin, I. A., Blatov, V. A. (2007). Acta Cryst. B63, 791-802.
1551 organic frameworks
Uninodal Edge-transitive
dia 22.6%
sxd 6.3%
pcu 9.8%
others 61.3%
Results: topological classification of nets
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Results: topological classification of nets
Valence-bonded metal-organic frameworks
dia
–
pcu
–
srs
Hydrogen-bonded single organic networks
dia –
pcu –
sxd –
hex
Hydrogen-bonded single metal- organic networks
pcu
–
bcu
–
hex –
dia
Hydrogen-bonded interpenetrated organic networks
dia –
cds –
lvt
Hydrogen-bonded interpenetrated metal-organic networks
pcu
–
dia –
cds
All the nets are uninodal, and all but cds, hex and sxd
are edge-transitive, all are high-symmetric
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Results: relations between structures
284 My
(TO4
)z
(T=Si, Ge, P, As, S, Se, Cl, Br, I)
quasi-binary representation My
[T]z
({M,T}, , {O}, {T})
50% My
(TO4
)z
correspond to binary compounds (NaCl, NiAs, FeB, -Al2
O3
, etc.)
Ilyushin
G.D., Blatov, V. A., Zakutkin
Yu.A.
(2004). Z. Krislallogr. 219, 468-478.
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Results: tilings
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Results: Topological types of intermetallics
ICSD~ 125000 entries
CRYSTMET~ 30000 entries
Pearson’s Crystal Data~ 105000 entries
TOPOS database on intermetallics ~ 22545 entries
Topological types of intermetallics ~ 3000 entries
Type Number %f.c.c. 3008 13.3b.c.c. 2480 11.0MgCu2 1678 7.4h.c.p. 875 3.9CuZn3 781 3.5
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Results: finite fragments in crystals
49-atom cluster
in Ag17
Mg44
Cambridge Cluster Database
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Results: finite fragments in crystals
R4
M42
606 structures
R6
M44
84 structures
R8
M78
16 structures
Cu4 Cd3 , Fe11 Zn40 , Ir4 Mg29 , Na6 Tl
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Results: constancy of atomic volume in crystals
‘…the volume of the domain of an atom of a definite chemical type at a fixed oxidation state, electronic configuration, and type of neighbouring atoms is
almost constant in different structures.’ Blatov, V.A. and Serezhkin, V.N. (2000)
Voronoi polyhedra in tens of thousands structures
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Summary and outlook
Local properties Stereochemistry
Global properties Crystal chemistry
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Summary and outlook
Crystal chemistry
Crystal chemistry
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Thanks for your attention!