Machine Learning Force Fields for Predicting Thermodynamic Properties of PA6T/6I Copolymers (Communications in Computational Chemistry, 2025)
This study employed molecular dynamics (MD) simulations, utilizing both a machine learning force field (MACE-OFF) and a traditional force field (PCFF), to predict the thermal properties of poly(hexamethylene terephthalamide-co-isophthalamide) (PA6T/6I) copolymers. The simulations are benchmarked against experimental data to assess the predictiveaccuracy of these two methodologies for the thermal properties of PA6T/6I copolymers. Our findings reveal that the MACE-OFFforce field, after calibration for the PA6T/6I copolymer, offers significant precision in modeling π-π and hydrogen-bondinginteractions, closely mirroring the results from M06 functional simulations. The MD simulations underscore the MACE-OFFmodel's ability to deliver more stable thermal properties, including the glass transition temperature (Tg) and density, for copolymersystems with varying PA6T content, aligning well with experimental observations. Furthermore, a comprehensive analysis ofdynamic properties, such as mean squared displacement and free volume, within the PA6T/6I copolymers was performed todecipher the mechanisms underlying the temperature-dependent changes in thermal properties observed throughout the simulationprocess. A thorough examination of the fluctuations in inter-chain and intra-chain hydrogen bonding within the copolymer systemshas unveiled the correlation between the molecular packing arrangement and thermal properties. This research establishes that theMACE-OFF model accurately simulates the thermal dynamical behavior of PA6T/6I copolymers, a capability that could beextended to other polyamide systems.