Thinking about solutions to such experimental bottlenecks can find inspiration from recent advances in computation and theory. In many ways, similar issues are encountered with the de-facto tool for simulating block copolymer phase behavior, such as self-consistent field theory (SCFT) and coarse-grained model. Yet the SCFT and other simulation ap-proaches rely on idealized parameters and assumptions that may not capture the distinct phase behaviors of block copolymers with different monomers. In addressing these challenges, recent advances in machine learning have created new opportunities to automate structural analysis by detecting patterns in datasets with high dimensionality. For example, Jayaraman and coworkers developed a high-throughput machine learning enhanced computational method which analyzes SAXS patterns to reconstruct real-space 3D structures of materials. Pozzo and colleagues introduced an algorithm which analyzes the shape of SAXS profiles to automatically generate phase maps. Furthermore, Olsen and colleagues leveraged a random forest model to analyze the phase behavior of diblock copolymers using experimental data mined from literature. While Olsen's method is effective in predicting phases of existing polymers in the database, this method, which we refer to as a chemistry-dependent random forest (CD-RF), relies on chemical-specific features, such as monomer identities, volume fraction, molecular weight, and temperature, which limits the predictive accuracy of phases formed by new monomers that are not in the database.