Interchain coupling and vibrational mode analysis of polytetrafluoroethylene using machine-learned potentials

Abstract

We investigate the temperature-dependent vibrational properties of crystalline polytetrafluoroethylene (PTFE) using molecular dynamics simulations powered by a neural-network potential that explicitly incorporates long-range van der Waals (vdW) interactions. Our simulations reveal a systematic red shift in three vibrational bands (800–700, 680–640, and 385–360 cm−1) as temperature increases. To elucidate the microscopic origin of these shifts, we perform phonon calculations under distinct structural scenarios, including helical unwinding, helix reversal defects, and controlled expansion of in-plane lattice constants. Only the increase in interchain distance reproduces the observed shifts, indicating that these modes are sensitive to intermolecular coupling. Eigenmode analysis shows that these redshifting bands are dominated by symmetric CF2 stretching motions with transverse fluorine displacements, which directly modulate interchain separation. In contrast, torsional, bending, and asymmetric stretching modes exhibit negligible frequency changes. These findings demonstrate that a specific class of vibrational modes serves as a microscopic probe of intermolecular interactions in PTFE and underscore the importance of incorporating long-range vdW corrections into machine-learned potentials for accurate vibrational modeling of polymeric and molecular crystals.