A bottom-up design strategy for controllable self-assembly based on the isotropic double-well potential.

Controllable self-assembly of particles or atoms is still challenging in the synthesis of materials with desirable properties that are highly relevant to the microscopic structures determined by the interparticle interactions. To gain insight into how the interactions affect the self-assembly, we designed various kinds of isotropic double-well potentials and simulated the motion of the particles. By controlling the depth and location of the potential wells and the height of the barriers, we studied their effects on the aggregation structures and the related microscopic kinetic processes. We identified five aggregation patterns at different temperatures and eight kinds of crystals, including Frank-Kasper phases, and observed the expansion or contraction of crystals. We found that the system usually stays in a sparse configuration at very low or very high temperatures. The particles typically assemble into a loosely packed cluster at medium temperatures and then deplete into a tightly packed state with a specific pattern. These phenomena can be explained from the perspective of energy. In contrast, very few structures could be obtained for the system guided by a single-well potential under the same simulation conditions. Thus, the interparticle interactions driven by the double-well potential greatly enrich the possible packing morphology of the system. The information obtained from this work helps us to understand how to achieve a specific self-assembled architecture through a reasonable selection of materials.

Spatial heterogeneity and infection patterns on epidemic transmission disclosed by a combined contact-dependent dynamics and compartmental model.

Epidemics, such as COVID-19, have caused significant harm to human society worldwide. A better understanding of epidemic transmission dynamics can contribute to more efficient prevention and control measures. Compartmental models, which assume homogeneous mixing of the population, have been widely used in the study of epidemic transmission dynamics, while agent-based models rely on a network definition for individuals. In this study, we developed a real-scale contact-dependent dynamic (CDD) model and combined it with the traditional susceptible-exposed-infectious-recovered (SEIR) compartment model. By considering individual random movement and disease spread, our simulations using the CDD-SEIR model reveal that the distribution of agent types in the community exhibits spatial heterogeneity. The estimated basic reproduction number R0 depends on group mobility, increasing logarithmically in strongly heterogeneous cases and saturating in weakly heterogeneous conditions. Notably, R0 is approximately independent of virus virulence when group mobility is low. We also show that transmission through small amounts of long-term contact is possible due to short-term contact patterns. The dependence of R0 on environment and individual movement patterns implies that reduced contact time and vaccination policies can significantly reduce the virus transmission capacity in situations where the virus is highly transmissible (i.e., R0 is relatively large). This work provides new insights into how individual movement patterns affect virus spreading and how to protect people more efficiently.