Extremely High Thermal Conductivity of Aligned Polyacetylene Predicted using First-Principles-Informed United-Atom Force Field
Teng Zhang, Jiaxin Xu, Tengfei Luo

TL;DR
This study develops a first-principles-informed force field for polyacetylene, enabling molecular dynamics simulations that predict extremely high and length-dependent thermal conductivities surpassing other polymers.
Contribution
The paper introduces a novel, accurately parameterized force field for polyacetylene based on DFT, facilitating reliable thermal conductivity predictions through molecular dynamics.
Findings
Polyacetylene exhibits very high thermal conductivity (~480 W/mK for single chains).
Thermal conductivity of polyacetylene is length-dependent, increasing with chain length.
Predicted thermal conductivities surpass those of polyethylene, the most conductive polymer fiber.
Abstract
Molecular simulations of polymer rely on accurate force fields to describe the inter-atomic interactions. In this work, we use first-principles density functional theory (DFT) calculations to parameterize a united-atom force field for polyacetylene (PA), a conjugated polymer potentially of high thermal conductivity. Different electron correlation functionals in DFT have been tested. Bonding interactions for the alternating single and double bonds in the conjugated polymer backbone are explicitly described and Class II anharmonic functions are separately parameterized. Bond angle and dihedral interactions are also anharmonic and parameterized against DFT energy surfaces. The established force field is then used in molecular dynamics (MD) simulations to calculate the thermal conductivity of single PA chains and PA crystals with different simulation domain lengths. It is found that the…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsThermal properties of materials · Fuel Cells and Related Materials · Machine Learning in Materials Science
