  Using methods borrowed from machine learning we detect in a fully algorithmic
way long range effects on local physical properties in a simple covalent system
of carbon atoms. The fact that these long range effects exist for many
configurations implies that atomistic simulation methods, such as force fields
or modern machine learning schemes, that are based on locality assumptions, are
limited in accuracy. We show that the basic driving mechanism for the long
range effects is charge transfer. If the charge transfer is known, locality can
be recovered for certain quantities such as the band structure energy.
