We investigate a set of design principles that link specific features of interparticle interactions to predictable structural and dynamic outcomes in two-dimensional self-assembly, a framework relevant to soft matter and biological condensates. Using extensive Molecular Dynamics simulations of single- and two-component systems, we systematically dissect how modifications to competing short-range attraction and long-range repulsion (SALR) potentials (both isotropic and anisotropic) serve as independent control parameters. In particular, we have focused on tuning the repulsive barrier height, decorating the attractive well with oscillatory components, and changing particle geometry. We demonstrate that these modifications dictate cluster size distributions, the degree of intra-cluster ordering, the geometry of the clusters, and the propensity for inter-cluster crystallization. A key finding is the decoupling of internal and global dynamics: oscillatory wells promote solid-like order within clusters while maintaining liquid-like cluster mobility. Furthermore, we show how asymmetric interactions in a binary SALR mixture can be designed to induce internal phase segregation within condensates. Complementing this, we observe that in anisotropic models in which the short rage component of the interaction stems from the presence of attractive patchy sites, stoichiometry and the geometric distribution of the patches are essential to control self-assembly and cluster morphology, whereas long-range repulsion can be used to tune cluster size and polydispersity. The extracted principles provide a causal road-map for engineering self-assembled materials and a set of basic physical concepts for interpreting the complex phase behavior of biomolecular condensates.
Correspondence between time-evolution dynamics of a tumor and an attractively interacting Bose-Einstein Condensate with feeding and dissipation
预期肿瘤的形态和时间演变在很大程度上取决于细胞组装的整体物理学的细致平衡（例如，动力学压力，细胞-细胞相互作用，和外部捕获的组织）和有丝分裂，坏死等生物过程，在这里，我们第一次，包括这样一个细致平衡在一个理论模型，利用{ \it ab initio}的数学框架原子玻色-爱因斯坦凝聚（BEC）的喂养和耗散，研究肿瘤的演变动力学。我们发现，与特征长度缩放，Gross-Pitaevskii（GP）方程，它描述了多体原子BEC特性，确实解释了血管前肿瘤培养数据的详细功能。该协议表明，血管前癌可能是一个天然的类似物BEC和预测细胞间波连接的细胞。
