Prediction of Block Copolymer Phase Behavior Using Machine Learning

Self-consistent field theory (SCFT) provides valuable insights into the driving forces behind microphase separation in block copolymers. While it is qualitatively highly accurate, it suffers quantitative limitations: increased error near the order-disorder transition, inability to capture strong asymmetry in experimental phase diagrams, and reliance on the Flory-Huggins interaction parameter to quantify block incompatibility with complex functional dependence on temperature, chemistry, and composition. Given the challenges in physics-based methods, we develop a purely data-driven model to predict phase behavior for neat diblock copolymers and compare with SCFT. First, we collect over 5000 experimental phase measurements from literature, recording the chemistry, molar mass, volume fraction, and temperature for each phase in a database. Then, we train a random forest classification model to predict the disordered, lamellar, cylindrical, gyroid, and spherical phases. This model, when adjusted for uncertainty in molecular parameters, is significantly more accurate than SCFT predictions with the Flory-Huggins interaction parameter calculated from group contribution theory. This work demonstrates the value that machine learning brings to soft matter materials design.