A proficient multivariate approach for iron(II) spin crossover behaviour modelling in the solid state.

Iron(II) -pyrazolilpyridyl (bpp-R) complexes Fe(bpp-R)·solvent, R = substituent and X = anion, can undergo a spin transition from high ( = 2, HS) to low spin ( = 0, LS), being spin crossover (SCO) in the solid state. The distortion of the octahedral coordination environment around the metal centre is governed by crystal packing, the intermolecular interactions among the substituent R of the bpp-R ligands, the anion X, and the co-crystallized solvent, and this modulates the SCO behaviour. In this work, an innovative multivariate approach, through the combination of the chemometric tools Principal Component Analysis and Partial Least Squares regression, was applied on the coordination bond distances and angles and selected torsional angles of the available HS structures. The obtained results can efficiently model and rationalize the structural data distinguishing between SCO-active and HS-blocked complexes bearing different R groups, X anions, and co-crystallized solvents and help predict the spin transition temperature .