In molecular dynamics, Machine Learning potentials (MLPs) have seen tremendous success when trained bottom-up on ab initio forces and energies. MLPs enable simulation times out of reach for ab initio computations at accuracies out of reach for classical force fields. However, due to the underlying approximations when solving the Schrödinger equation, MLPs sometimes fail to quantitatively reproduce experimental data. On the other hand, training MLPs top-down on experimental target properties yields largely under-constrained force fields that fail to reproduce many off-target properties for which bottom-up models yield better results. To overcome these limitations, we present a combined bottom-up and top-down learning approach using titanium as a showcase. We show that a fused training approach yields an MLP in close agreement with DFT and experimental targets. Moreover, the fused model generalizes to several off-target properties and often performs better than training only on DFT data. The presented approach is general and applicable to generate highly accurate MLPs for other materials.