Investigating the Impact of Bidispersity on Spin-Coated Polystyrene Thin Films Using Machine Learning

Abstract

The thickness of spin-coated polystyrene (PS) thin films largely influences their physical properties. While industrial applications primarily utilize polydisperse PS, existing models address only monodisperse systems. We created bidisperse PS films across varying concentrations, molecular weights, and blend ratios, evaluated monodisperse models with bidisperse data, and tested other machine learning models. We discovered that bidisperse films were systematically thinner than monodisperse films of equivalent weight-average molecular weight, with the overlap parameter (c/c*) emerging as a key predictor. Our Gaussian Process Regression achieved MAPE = 3.82% (63% improvement over monodisperse models), R² = 0.9919, and RMSE = 75.2 Å.

Conferences, Distinctions, and Publications

• Published in MRS Communications (April 2026), following a ChemRxiv preprint (February 2026).
• Presented at the Garcia Scholars Program end-of-summer symposium (August 2024). See our abstract in the yearbook (p. 67).
• Oral presentation at the Materials Research Society Fall Meeting (December 2024). MRS abstract
• Oral presentation at the American Physical Society Global Physics Summit (March 2025). APS abstract