We investigate the selective forces that promote the emergence of modularity in nature. We demonstrate the spontaneous emergence of modularity in a population of individuals that evolve in a changing environment. We show that the level of modularity correlates with the rapidity and severity of environmental change. The modularity arises as a synergistic response to the noise in the environment in the presence of horizontal gene transfer. We suggest that the hierarchical structure observed in the natural world may be a broken-symmetry state, which generically results from evolution in a changing environment.

The presence of excess hydrogen at the interface between a metal substrate and a protective oxide can cause blistering and spallation of the scale. However, it remains unclear how nanoscale bubbles manage to reach the critical size in the first place. Here, we perform in situ environmental transmission electron microscopy experiments of the aluminium metal/oxide interface under hydrogen exposure. It is found that once the interface is weakened by hydrogen segregation, surface diffusion of Al atoms initiates the formation of faceted cavities on the metal side, driven by Wulff reconstruction. The morphology and growth rate of these cavities are highly sensitive to the crystallographic orientation of the aluminium substrate. Once the cavities grow to a critical size, the internal gas pressure can become great enough to blister the oxide layer. Our findings have implications for understanding hydrogen damage of interfaces.

The martensitic transformation serves as the basis for applications of shape memory alloys (SMAs). The ability to make rapid and accurate predictions of the transformation temperature of SMAs is therefore of much practical importance. In this study, we demonstrate that a statistical learning approach using three features or material descriptors related to the chemical bonding and atomic radii of the elements in the alloys, provides a means to predict transformation temperatures. Together with an adaptive design framework, we show that iteratively learning and improving the statistical model can accelerate the search for SMAs with targeted transformation temperatures. The possible mechanisms underlying the dependence of the transformation temperature on these features is discussed based on a Landau-type phenomenological model.

Topographical patterns endow material surfaces with unique and intriguing physical and chemical properties. Spontaneously formed wrinkling has been harnessed to generate surface topography for various functionalities. Despite promising applications in biomedical devices and robot engineering, the friction behavior of wrinkling on curved surfaces remains unclear. Herein, wrinkled surfaces are induced by sputtering metals on soft polymer microspheres. The wrinkle morphologies and length scales can be controlled precisely by tailoring the microsphere radius (substrate curvature) and film thickness. The wrinkled surfaces exhibit controlled friction property, depending on the wrinkling patterns and length scales. An increase in friction force with increasing surface roughness is generally found for dimple patterns and labyrinth patterns. The dimple patterns show the lowest friction due to strong curvature constraint. The herringbone patterns exhibit apparent friction anisotropy with respect to topographic orientation. These results will guide future design of wrinkled surfaces for friction by harnessing substrate curvature.

We investigate the selective forces that promote the emergence of modularity in nature. We demonstrate the spontaneous emergence of modularity in a population of individuals that evolve in a changing environment. We show that the level of modularity correlates with the rapidity and severity of environmental change. The modularity arises as a synergistic response to the noise in the environment in the presence of horizontal gene transfer. We suggest that the hierarchical structure observed in the natural world may be a broken symmetry state, which generically results from evolution in a changing environment. To support our results, we analyze experimental protein interaction data and show that protein interaction networks became increasingly modular as evolution proceeded over the last four billion years. We also discuss a method to determine the divergence time of a protein.