  The rapid design of advanced materials is a topic of great scientific
interest. The conventional, ``forward'' paradigm of materials design involves
evaluating multiple candidates to determine the best candidate that matches the
target properties. However, recent advances in the field of deep learning have
given rise to the possibility of an ``inverse'' design paradigm for advanced
materials, wherein a model provided with the target properties is able to find
the best candidate. Being a relatively new concept, there remains a need to
systematically evaluate how these two paradigms perform in practical
applications. Therefore, the objective of this study is to directly,
quantitatively compare the forward and inverse design modeling paradigms. We do
so by considering two case studies of refractory high-entropy alloy design with
different objectives and constraints and comparing the inverse design method to
other forward schemes like localized forward search, high throughput screening,
and multi objective optimization.
