The development of Machine Learning Interatomic Potentials (MLIPs) has gained significant traction in recent years. These new data-driven potential energy approximations often lack the physics-based foundations that inform many traditionally-developed interatomic potentials and hence require robust validation methods for their applicability, accuracy, computational efficiency, and transferability to the intended applications. This work presents a sequential, three-stage workflow for MLIP validation: (i) preliminary validation, (ii) static property prediction, and (iii) dynamic property prediction. This material-agnostic procedure is demonstrated for the development of a robust MLIP for boron carbide (B4C), a widely employed, structurally complex ceramic that undergoes a deleterious deformation mechanism called ‘amorphization’ under high pressure loading. It is shown that the resulting B4C MLIP offers a more accurate prediction of properties and behaviors with increased computational efficiency compared to the available ReaxFF potential.