Hybrid machine learning/materials science modeling for semi-crystalline polymer during film fabrication process

For semi-crystalline polymer like polyethylene (PE), it is well known that PE film physical properties is heavily dependent on the morphology of both the crystalline phase and amorphous chains, which can be largely influence by the film processing conditions. A clear understanding of the relationships of polymer molecular fingerprint, formulation, fabrication conditions and physical properties is important for future materials design, which can be traced back to polymerization process. However, this is generally considered to be a very complicated problem due to the large parameter space. In this report, we developed a new hybrid approach to combine the power of machine learning and fundamental materials science to characterize semi-crystalline PE, develop structure-property relationship and study the effect of fabrication conditions on physical properties during blown film fabrication process and to inform the design of new polymer structures.