  We employ machine learning techniques to investigate the volume minimum of
Sasaki-Einstein base manifolds of non-compact toric Calabi-Yau 3-folds. We find
that the minimum volume can be approximated via a second order multiple linear
regression on standard topological quantities obtained from the corresponding
toric diagram. The approximation improves further after invoking a
convolutional neural network with the full toric diagram of the Calabi-Yau
3-folds as the input. We are thereby able to circumvent any minimization
procedure that was previously necessary and find an explicit mapping between
the minimum volume and the topological quantities of the toric diagram. Under
the AdS/CFT correspondence, the minimum volumes of Sasaki-Einstein manifolds
correspond to central charges of a class of 4d N=1 superconformal field
theories. We therefore find empirical evidence for a function that gives values
of central charges without the usual extremization procedure.
