Multi-scale theoretical tools for in silico macromolecular chemistry and engineering

Polymers are composed of macrospecies that are characterized by a broad range of molecular variations such as chain length, functionality, comonomer composition, branching density, and crosslinking amount that can be controlled through (i) a plethora of macromolecular chemistries and (ii) many reactor configurations and operations. To fully grasp and exploit these control handles, it is strongly recommended (i) to develop modeling tools that are synergetic with experimental methods and (ii) to identify which in silico tool is suited most in which context. In the present chapter, we give an overview of the most important in silico multiscale tools for macromolecular chemistry and engineering. This is done by addressing both deterministic and stochastic modeling approaches, allowing application at both lab and industrial scale. We first explain how computational chemistry (e.g., ab initio calculations) can contribute to the understanding and tuning of chemical reactivities and chain interactions, thus molecular scale phenomena. This illustrated through a Case study 2.1 on controlled radical polymerization. We then introduce the concept of microscale modeling to map the competition of chemistry and diffusional limitations, as polymerization processes are very prone to viscosity increases. Specific emphasis is on the benchmarking of the popular method of moments and the recently more employed matrix-based kinetic Monte Carlo simulations (Case study 2.2) as well as machine learning tools. Mesoscale modeling tools are subsequently discussed to enable the description of multiphase/particulate polymerizations such as emulsion polymerization or the synthesis of high impact polymeric materials. A Case study 2.3 is devoted to surface initiated polymerization. We further elaborate on macro-scale modeling tools that allow to account for scale-up effects, due to nonhomogeneity in temperature and mixing intensities. Here we specifically deal with the recent advances in computational fluid dynamics (CFD) simulations, as illustrated through a Case study 2.4. Finally, we link the already introduced multiscale polymer reaction engineering approaches with the field of materials science.Polymers are composed of macrospecies that are characterized by a broad range of molecular variations such as chain length, functionality, comonomer composition, branching density, and crosslinking amount that can be controlled through (i) a plethora of macromolecular chemistries and (ii) many reactor configurations and operations. To fully grasp and exploit these control handles, it is strongly recommended (i) to develop modeling tools that are synergetic with experimental methods and (ii) to identify which in silico tool is suited most in which context. In the present chapter, we give an overview of the most important in silico multiscale tools for macromolecular chemistry and engineering. This is done by addressing both deterministic and stochastic modeling approaches, allowing application at both lab and industrial scale. We first explain how computational chemistry (e.g., ab initio calculations) can contribute to the understanding and tuning of chemical reactivities and chain interactions, thus molecular scale phenomena. This illustrated through a Case study 2.1 on controlled radical polymerization. We then introduce the concept of microscale modeling to map the competition of chemistry and diffusional limitations, as polymerization processes are very prone to viscosity increases. Specific emphasis is on the benchmarking of the popular method of moments and the recently more employed matrix-based kinetic Monte Carlo simulations (Case study 2.2) as well as machine learning tools. Mesoscale modeling tools are subsequently discussed to enable the description of multiphase/particulate polymerizations such as emulsion polymerization or the synthesis of high impact polymeric materials. A Case study 2.3 is devoted to surface initiated polymerization. We further elaborate on macro-scale modeling tools that allow to account for scale-up effects, due to nonhomogeneity in temperature and mixing intensities. Here we specifically deal with the recent advances in computational fluid dynamics (CFD) simulations, as illustrated through a Case study 2.4. Finally, we link the already introduced multiscale polymer reaction engineering approaches with the field of materials science.B