From Quantum Mechanics to Coarse-Grained Models: Bridging the Gap toward Polymer Rational Design.

Area of Science:

• Computational materials science
• Polymer physics
• Molecular modeling

Background:

• Classical physics-based simulation methods for polymer design face limitations in accuracy and transferability for full-atomistic (FA) and coarse-grained (CG) models.
• Existing models often struggle to accurately predict polymer properties due to these inherent limitations.

Purpose of the Study:

• To develop a first-principles-based, modular computational protocol for generating accurate and consistent FA and CG force fields.
• To create a unified framework connecting Quantum Mechanical (QM) calculations, QM-derived force fields (QMD-FFs), and Molecular Dynamics (MD) simulations.
• To enable the in silico design of novel polymers with improved accuracy and predictability.

Main Methods:

• A novel computational protocol utilizing QM calculations as the foundation for generating FA and CG force fields.
• Integration of QM calculations, FA/CG QMD-FFs, and MD simulations into a single, reproducible workflow.
• Testing the protocol on poly(ethylene terephthalate) (PET) as a representative polymer material.

Main Results:

• MD simulations using the developed FA and CG QMD-FFs significantly outperformed standard models in predicting PET properties.
• Key properties accurately predicted include density, glass transition temperature, and intra-/supra-molecular structure.
• Improvements are attributed to the accuracy of the underlying QM calculations and the controlled information flow across scales.

Conclusions:

• The developed protocol provides a reliable, general, and tunable approach for generating accurate polymer force fields.
• This method serves as a promising tool for the in silico rational design of novel polymers.
• Further automation could enable integration with machine learning for high-throughput polymer discovery.