<p>Climate change, resource scarcity and energy transition are some of the biggest drivers for new material innovations that are needed in ever-increasing pace. Material acceleration platforms (MAPs) are suggested as a solution to accelerate new material development. Combining integrated computational materials engineering, artificial intelligence (AI), high throughput sample preparation, characterization, and testing, we present our effort to develop the SOLID-MAP for metallic materials such as high-entropy alloys (HEA) in this paper. We started the development with active learning-based surrogate modeling and CALPHAD simulation that were utilized to screen application-specific chemical compositions. The computational thermodynamic screening was complemented by first-principles density functional theory simulations, to provide an initial mapping of the HEAs based on their mechanical properties, enabling the down selection of a set of HEAs for experimental realization. High throughput direct energy deposition was used to fabricate samples of these novel alloys from elemental unmixed powders using optimized process parameters on a single steel substrate. Finally, the SOLID-MAP process was completed by investigating the as-printed samples using automated x-ray diffraction, electron microscopic characterization and automated analyses of these measurements using AI-based models. Our preliminary results indicate a significant speed-up in new HEA development.</p>