Chemically informed fragment choices to improve the property prediction for polymer systems

Disordered systems such as polymers pose particular challenges to computational modeling. The more disordered the 3-dimensional structure of a macromolecule, the more atoms must be included in a computational model to achieve representative results, which can be intractable with many accurate quantum methods. Fragmentation approaches overcome this challenge by partitioning the calculation into manageable subsystems with a subsequent merging of the results to reproduce the supersystem prediction. Fragments must be chosen judiciously to balance reduced computational scaling (smaller fragments) and accuracy (larger fragments). Fragment choice is often based on established chemical functional groups or by manual inspection, which may fail to capture important chemical interaction and can be time consuming for the user. We propose an automatic fragmentation approach in which each fragment is chosen according to quantitative criteria based on electronic-structure information achieving a systematically improvable molecular partitioning. This presentation discusses progress towards the first principles predictions of electronic polarizabilities in oligomer systems for this fragmentation approach.