For ML to become a viable replacement for current methods, ML needs to achieve optimized geometries and predict properties without relying on force field (FF) methods. Most FFs have been refined for small molecules and biomolecules and can struggle with non-covalent and steric interactions for applications such as conjugated polymers. While these issues can be lessened with specific parameterization, 25,26 geometries of FFs generally can be less than ideal. ML trained on higher levels of theory ideally captures these non-covalent interactions and provides better initial optimized geometries.