Artificial reverberation algorithms often suffer from spectral coloration, usually in the form of metallic ringing, which impairs the perceived quality of sound. This paper proposes a method to reduce the coloration in the feedback delay network (FDN), a popular artificial reverberation algorithm. An optimization framework is employed entailing a differentiable FDN to learn a set of parameters decreasing coloration. The optimization objective is to minimize the spectral loss to obtain a flat magnitude response, with an additional temporal loss term to control the sparseness of the impulse response. The objective evaluation of the method shows a favorable narrower distribution of modal excitation while retaining the impulse response density. The subjective evaluation demonstrates that the proposed method lowers perceptual coloration of late reverberation, and also shows that the suggested optimization improves sound quality for small FDN sizes. The method proposed in this work constitutes an improvement in the design of accurate and high-quality artificial reverberation, simultaneously offering computational savings.