Regularities of formation and lattice distortion of perovs-kite-type compounds

Based on 489 known perovskite-type complex oxides and a number of other type complex oxides, the pattern recognition-atomic parameter method is adopted to find regularities of the formation and the lattice distortion of the perovskite structure. It has been found that the restriction on Goldschmidt’s t factor constitutes only a necessary but not a sufficient condition to form perovskite-type compounds. A more effective mathematical model, which can precisely sum up the regularities of the formation, the lattice distortion, and the cell constants of known perovskite-type compounds and reliably make corresponding predictions on unknown compounds, can be set up by integrating multiple atomic parameters such as ionic radii, ionic valency, and Basanov’s electronegativity of constituent elements. Based on it, an intelligent database has been implemented. Its prediction accuracy is tested by eight newly discovered perovskite-type compounds such as Eu(Mn0.5 Ni0.5)O3, etc. (they are not included in the database

Regularities of formation of binary intermetallic compounds between transition and non-transition elements

A four-parameter model based on the extended Miedema’s cellular model of alloy phases and pattern recognition methods has been used to study the regularities of the formation of binary intermetallic compounds between tran-sition element and non-transition element. The formation criterion can be expressed as some inequities of electronega-tivity , the valence electron density in Wagner-Seitz cell nws1/3, Pauling’s metallic radius R and the number of valence electrons in atom Z or their functions. According to these empirical criterions, the 'unknown' binary alloy system can be predicted, the predicted result is better than that of Miedema’s two-parameter model.

PCA for predicting quaternary structure of protein

The number and arrangement of subunits that form a protein are referred to as quaternary structure.Knowing the quaternary structure of an uncharacterized protein provides clues to finding its biological function and interaction process with other molecules in a biological system.With the explosion of protein sequences generated in the Post-Genomic Age,it is vital to develop an automated method to deal with such a challenge.To explore this prob-lem,we adopted an approach based on the pseudo position-specific score matrix(Pse-PSSM)descriptor,proposed by Chou and Shen,representing a protein sample.The Pse-PSSM descriptor is advantageous in that it can combine the evolution information and sequence-correlated informa-tion.However,incorporating all these effects into a descriptor may cause‘high dimension disaster’.To over-come such a problem,the fusion approach was adopted by Chou and Shen.A completely different approach,linear dimensionality reduction algorithm principal component analysis(PCA)is introduced to extract key features from the high-dimensional Pse-PSSM space.The obtained dimension-reduced descriptor vector is a compact repre-sentation of the original high dimensional vector.The jack-knife test results indicate that the dimensionality reduction approach is efficient in coping with complicated problems in biological systems,such as predicting the quaternary struc-ture of proteins.