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Phase refinement from a partially known structure using linear prediction filtering
Author(s) -
Tang J.,
Norris J. R.
Publication year - 2009
Publication title -
international journal of quantum chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.484
H-Index - 105
eISSN - 1097-461X
pISSN - 0020-7608
DOI - 10.1002/qua.560360706
Subject(s) - electron density , phase (matter) , noise (video) , identification (biology) , least squares function approximation , algorithm , computer science , experimental data , statistical physics , electron , biological system , physics , mathematics , artificial intelligence , quantum mechanics , statistics , botany , estimator , image (mathematics) , biology
A significantly improved electron‐density map can be calculated from an incomplete, approximate structure using linear prediction filtering (LPF). The filtering procedure is not based on constraining the electron density to be positive, but, instead, rests on a linear relation of the structure factor. In two examples, one based on theoretical data and the other based on experimental data, these new LPF maps have lower background noise and improved quality than do the more conventional 2F o – F c technique. The recovered electron‐density profile from atoms initially absent is clearer, allowing for easier identification in any subsequent model building and least‐squares refinement.