The QEq approach as outlined above only requires three parameters, namely the electronegativity (χ A ), the non-classical contribution to the hardness (η A ) and the atomic size (α A ) for each species in the system. As a flipside of this elegant simplicity, the accuracy and transferability of the QEq method is limited, however. In the kernel Charge Equilibration (kQEq) method proposed herein, we follow the basic idea of Goedecker and coworkers to overcome this limitation. This is achieved by allowing the electronegativity of an atom to change as a function of its chemical environment. Importantly, taking advantage of the fact that both QEq and Kernel Ridge Regression (KRR) are formulated as linear problems, we obtain a closed-form expression for training these models.