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At this point in time, material design projects arise from qualitative ideas which are inpired in the chemistry of existing compounds. [ Should we say that in the future machine learning might give even more radical points of departure ? ].  $Ba_{1-x} K_x B iO_3$, is a famous high temperature superconductor, discovered in the later 80's. \cite{Nourafkan_2012}[ 80's.[  REF SLEITHGT AND CAVA]. Its parent compound, BaBiO3, has a distorted  perovskite structure, with a gap of the order of   0.7 eV band gap 
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the structural distortions and makes the material superconducting   superconducting reaching a maximum critical temperature of the order of  30 K (Cava et.al.  (1988))at the optimal doping. LDA fails to describe the insulating character of the parent compound. Conventional LDA estimates of the electron phonon coupling, within Migdal Eliasberg theory, predicted that the electron phonon coupling in the doped compound is of the order of XXX and therefore cannot account for its superconductivity. REFERENCE TO   SAVRASOV. \cite{Meregalli_1998}  In ref XXX we found that in BaBiO 3 there is a substantial  correlation enhancement of λ relative to its LDA estimate, and that this enhancement is