发表机构
Johns Hopkins University（约翰斯·霍普金斯大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出结合kMC模拟的计算框架FormLNP，可预测RNA-脂质和PEI-DNA纳米颗粒的单颗粒性质分布，揭示组装路径对异质性分布的影响。
AI 中文摘要
多组分纳米颗粒的组装通常受动力学控制，且表现出强路径依赖性。输运、溶剂交换、成核/生长以及碰撞驱动的聚结不仅决定了系综平均性质，还决定了颗粒间的组成异质性。本文提出一种计算建模框架，用于预测纳米颗粒性质分布，该框架将加工条件、早期自组装物理过程及分子化学细节与动力学蒙特卡洛（kinetic Monte Carlo, kMC）模拟相结合。该框架包含两部分：（i）结合溶剂交换介导的颗粒初始化与生长的混合条件；（ii）可解析随机碰撞历史、静电控制聚结及单颗粒水平组成的kMC模拟。将其应用于mRNA脂质纳米颗粒时，该模型可预测尺寸-负载量相关性，并揭示加工依赖的组装路径如何导致异质性负载分布。kMC模拟还提供了合并谱系历史，通过乘性颗粒生长路径解释了对数正态体积与负载分布的出现。该框架同样应用于PEI-DNA聚电解质络合，得到了单颗粒分辨率的DNA-PEI化学计量分布。该框架及其开源实现FormLNP，为预测和控制广泛多组分纳米颗粒体系的单颗粒性质分布提供了一种加工感知的途径。
英文摘要
The assembly of multicomponent nanoparticles is often kinetically controlled and exhibits strong pathway dependence. Transport, solvent exchange, nucleation/growth, and collision-driven coalescence together determine not only ensemble-averaged properties but also particle-to-particle compositional heterogeneity. Here, we present a computational modeling framework for predicting nanoparticle property distributions by coupling processing conditions, early-stage self-assembly physics, and molecular chemical details with kinetic Monte Carlo (kMC) simulations. The framework combines (i) mixing conditions with solvent-exchange-mediated particle initialization and growth, and (ii) kMC simulations that resolve stochastic collision histories, electrostatics-controlled coalescence, and composition at the level of individual particles. Applied to mRNA lipid nanoparticles, the model predicts size-loading correlations and provides insight into how processing-dependent assembly pathways lead to heterogeneous payload distributions. The kMC simulations further provide merging lineage histories, which explain the emergence of log-normal volume and payload distributions through multiplicative particle-growth pathways. The same framework is also applied to PEI-DNA polyelectrolytic complexation, yielding single-particle-resolved DNA-PEI stoichiometry distributions. The framework and its open-source implementation, FormLNP, provide a process-aware route to predicting and controlling single-particle property distributions across a broad range of multicomponent nanoparticle systems.
Comments14 pages, 7 figures
