Atomistic Polymer Modeling: Recent Advances and Challenges in Building and Parametrization Workflows.

Area of Science:

• Materials Science
• Computational Chemistry
• Polymer Science

Background:

• Synthetic polymers are versatile soft materials.
• Computational microscopy, including atomistic molecular dynamics (MD) simulations, aids in characterizing polymer structure-property relationships.
• Current MD simulation workflows for synthetic polymers lag behind those for biomolecules.

Purpose of the Study:

• To discuss recent advancements in system building and parametrization for synthetic polymer simulations.
• To identify unique challenges in modeling synthetic polymers compared to biopolymers.
• To outline improvements for established workflows and promote FAIR polymer simulations.

Main Methods:

• Review of current literature and simulation workflows for synthetic polymers.
• Identification of shortcomings in existing methods.
• Highlighting new computational tools and approaches, including cheminformatics, direct chemical perception, and neural networks.

Main Results:

• Established workflows for synthetic polymer modeling have limitations.
• Significant challenges exist in parameterizing and building complex synthetic polymer systems for simulations.
• Emerging tools show promise for enhancing the accuracy and efficiency of polymer simulations.

Conclusions:

• Improving force fields and workflows is crucial for accurate synthetic polymer simulations.
• New computational strategies are needed to address the complexities of synthetic polymer modeling.
• Adoption of FAIR data principles and advanced computational tools will advance the field.