Particle-based mesoscale modeling and coarse-graining methods

Understanding the thermomechanical behavior of graphene-reinforced conjugated polymer nanocomposites via coarse-grained modeling

“…It simultaneously preserves the key chemical features, improving the computational efficiency by decreasing the degree of freedom. 86 However, the energy landscape of the CG model is smoother than that of the atomistic model due to the inevitable loss of atomic friction and configurational entropy (s c ) upon coarse-graining, causing artificially accelerated dynamics and softer mechanical responses, [21][22][23][24] and further limiting the practical application of the CG model. For example, Huang et al 25 first developed a CG model of P3HT using a three-bead-per-monomer mapping scheme (three-site model); although their model captures the self-assembly and dynamic evolution of P3HT and its mixtures with C60, further study 26 revealed that this model would cause significant deviation in the density and diffusion coefficients compared to the AA counterpart at varying temperatures.…”

A Molecular Dynamics Study of Mechanical and Conformational Properties of Conjugated Polymer Thin Films

“…However, traditional CG models always overestimate the dynamics and underestimate the mechanical response of the system due to the smooth energy landscape and the loss of configurational entropy ( s c ) upon coarse-graining. , Huang et al and Lee et al previously developed the P3HT CG model using a three-site and one-site mapping scheme, providing insightful studies on the phase separation and morphologies of a P3HT/C60 mixture. However, the one-site P3HT model exhibited overestimated density and softer mechanical response compared with the experiment, and the three-site model showed deviated diffusion behavior under different temperatures compared with the target AA model, necessitating the accurate CG modeling of conjugated polymer. , To address these issues, we utilized the energy-renormalization (ER) approach in our recent work to systematically develop a temperature- and architecture-transferable CG model of P3ATs. − Specifically, by varying the cohesive interaction parameter ϵ and effective distance parameter σ of Lennard-Jones (LJ) potential in a temperature-dependent style, the ER method could compensate the s c loss of the system via renormalizing the system’s enthalpy (i.e., “entropy–enthalpy compensation” effect) and reproduce the AA density and dynamics over a wide temperature range.…”

“… 22 , 23 To address these issues, we utilized the energy-renormalization (ER) approach in our recent work to systematically develop a temperature- and architecture-transferable CG model of P3ATs. 24 − 26 Specifically, by varying the cohesive interaction parameter ϵ and effective distance parameter σ of Lennard-Jones (LJ) potential in a temperature-dependent style, the ER method could compensate the s c loss of the system via renormalizing the system’s enthalpy (i.e., “entropy–enthalpy compensation” effect) and reproduce the AA density and dynamics over a wide temperature range.…”

Energy renormalization for temperature transferable coarse-graining of silicone polymer

“…The CG model excels in efficiently simulating large molecular systems over extended spatiotemporal scales by integrating out unessential degrees of freedom upon coarse-graining. [11][12][13][14] Several coarse-graining approaches have previously been explored to overcome the spatiotemporal limitation and sustain the key chemical features of the underlying all-atomistic (AA) system, such as force matching, 15 inverse Monte Carlo, 12 inverse Boltzmann method, 16 top-down mapping, 17 or hybrid CG modelling. 18 However, it has been reported that coarse graining using these strategies also results in the reduction of fluid configurational entropy (S c ) and atomic friction due to the elimination of detailed atom information of AA models.…”