The Predicted Powder Diffraction Database P 2 D

The Predicted Powder Diffraction Database (P 2 D 2) Armel Le Bail u Maine, Laboratoire des Oxydes et Fluorures, CNRS UMR 6010, Av. O. Messiaen, 72085 Le Mans Cedex 9, France - Email : armel. le_bail@uni INTRODUCTION PCOD and P 2 D 2 Content Massive inorganic crystal structure predictions were recently Performed, justifying the creation of new databases. Among them, the PCOD [1] (Predicted Crystallography Open Database) contains the crystal data of predicted titanosilicates, phosphates, vanadates, niobates, fluoroaluminates (etc), by using the GRINSP [2] software. These databases open now the possibility for the identification of a newly synthesized compound by the comparison of its experimental powder pattern with predicted ones. The powder patterns calculated from the >100. 000 PCOD entries were gathered into the P 2 D 2 (Predicted Powder Diffraction Database) [3 -4], allowing for the identification of inorganic compounds by using any classical search-match software. Predicted crystal structures (from the PCOD) provide predicted fingerprints: calculated powder patterns, inserted into the P 2 D 2 Search-Match with EVA using the PCOD/P 2 D 2 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) : -Al. F 3, 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 (P 2 D 2) in 2 nd (green mark in the toolbox). Calculated powder patterns in the P 2 D 2 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 ; -) SEARCHING PCOD Search page Results : CIFs Example 2 – Model showing uncomplete chemistry, PAD = 0. 88. Predicted framework : Ca 4 Al 7 F 33, cubic, a = 10. 876 Å. Actual compound : Na 4 Ca 4 Al 7 F 33, 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 3 – Model showing uncomplete chemistry, PAD = 0. 63. Actual compound : K 2 Ti. Si 3 O 9 H 2 O, orthorhombic, a = 7. 136 Å, b = 9. 908 Å, c =12. 941 Å. Predicted framework : Ti. Si 3 O 9, 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 H 2 O are lacking in the PCOD model) : VIRTUAL MODELS in PCOD/P 2 D 2 Zeolite B 2 O 3 nanotubes CONCLUSION [Ca 3 Al 4 F 21]3 - To be successful, identification attempts require mainly accurate predicted cell parameters. Many improvements, by using empirical or ab initio approaches, will be needed in order to restrict the number of structure candidates to those having really a chance to exist (quite a difficult task). Prediction is obviously a large part of our future in crystallography, the P 2 D 2 represents a small new step in that direction, allowing to solve a crystal structure at the identification stage, when all structures will be predicted… REFERENCES [1] Predicted Crystallography Open Database – http: //www. crystallography. net/pcod/ [2] A. Le Bail, J. Appl. Cryst. 38 (2005) 389 -395. [3] A. Le Bail, Powder Diffraction 23 (2008) S 5 -S 12. [4] Predicted Powder Diffraction Database – http: //sdpd. univ-lemans. fr/cod/pcod/P 2 D 2/