Rational computer-aided design of multiphase polymer materials is vital for rapid progress in many important applications, such as: diagnostic tests, drug delivery, coatings, additives for constructing materials, cosmetics, etc. Several property predictive models, including the prospective Population Balance Model for Latex Particles Morphology Formation (LPMF PBM), have already been developed for such materials. However, they lack computational efficiency, and the accurate prediction of materials' properties still remains a great challenge. To enhance performance of the LPMF PBM, we explore the feasibility of reducing its complexity through disregard of the aggregation terms of the model. The introduced nondimensionalization approach, which we call Optimal Scaling with Constraints, suggests a quantitative criterion for locating regions of slow and fast aggregation and helps to derive a family of dimensionless LPMF PBM of reduced complexity. The mathematical analysis of this new family is also provided. When compared with the original LPMF PBM, the resulting models demonstrate several orders of magnitude better computational efficiency.

翻译：多相聚合物材料的理性计算机辅助设计对于诊断测试、药物递送、涂料、建筑材料添加剂、化妆品等许多重要应用的快速发展至关重要。尽管已有多种性能预测模型（包括面向乳胶颗粒形态形成的群体平衡模型，简称LPMF PBM）针对此类材料开发，但这些模型计算效率不足，材料性能的精确预测仍面临巨大挑战。为提升LPMF PBM的性能，我们探索通过忽略模型中的聚集项来降低其复杂性的可行性。引入的无量纲化方法（我们称之为带约束的最优缩放）为定位慢速与快速聚集区域提供了定量判据，并推导出具有降低复杂性的无量纲LPMF PBM族。我们还对该新模型族进行了数学分析。与原始LPMF PBM相比，所得模型的计算效率提升了数个数量级。