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Ensuring the correctness of scientific software is challenging due to the need to represent and model complex phenomenon in a discrete form. Many dynamic approaches for correctness have been developed for numerical overflow or imprecision, which may manifest as program crashes or hangs. Less effort has been spent on functional correctness, where one of the most widely proposed technique is metamorphic testing. Metamorphic testing often requires deep domain expertise to design meaningful relations. In this vision paper we ask if we can utilize the process of abstraction and refinement, a traditionally formal approach, to guide the development of metamorphic relations. We have built an iterative approach we call Model Assisted Refinements. It starts with domain-agnostic relations and a set of input-output relations created via a dynamic analysis. We then use a model checker to identify missing input/output patterns and potential passing and failing relations. We augment our dynamic analysis, and obtain domain expertise to verify and refine our relations. At the end we have a set of domain-specific metamorphic relations and test cases. We demonstrate our approach on a high-performance chemistry library. Within three refinements we discover several domain specific relations, and increase our behavioral coverage.