Nanoscale polymeric thin films are widely used in diverse modern applications, where a satisfactory mechanical performance is a requirement to their full functionality. The mechanical response of polymer films is strongly affected by the size effects under nanoconfinement; however, the mechanism of such response in terms of molecular configurations and chain conformations has yet to be determined. In this work, we reveal the conformational origin of the stiffening behavior of crosslinked polymeric nanofilms via coarse-grained molecular dynamics and tailored experiments. We find that the biaxial modulus changes follow the alteration of polymer conformations, decoupled from size and thickness. We propose a theory to quantitatively link the elastic properties of the polymers to the distribution of their chain end-to-end distances, predicting a stiffening effect on uncoiled chains. Finally, we use such insight to obtain several PDMS nano-films of the same thickness but with a variability of two orders of magnitudes in their moduli.

Generative design for materials has recently gained significant attention due to the rapid evolution of generative deep learning models. There have been a few successful generative design demonstrations of molecular-level structures with the help of graph neural networks. However, in the realm of macroscale material structures, most of the works are targeting two-dimensional, ungoverned structure generations. Hindered by the complexity of 3D structures, it is hard to extract customized structures with multiple desired properties from a large, unexplored design space. Here we report a novel framework, a multi-objective driven Wasserstein generative adversarial network (WGAN), to implement inverse designs of 3D structures according to given geometrical, structural, and mechanical requirements. Our framework consists of a WGAN-based network that generates 3D structures possessing geometrical and structural features learned from the target dataset. Besides, multiple objectives are introduced to our framework for the control of mechanical property and isotropy of the structures. An accurate surrogate model is incorporated into the framework to perform efficient prediction on the properties of generated structures in training iterations. With multiple objectives combined by their weight and the 3D WGAN acting as a soft constraint to regulate features that are hard to define by the traditional method, our framework has proven to be capable of tuning the properties of the generated structures in multiple aspects while keeping the selected structural features. The feasibility of a small dataset and the scalability of the objectives of other properties make our work an effective approach to provide fast and automated structure designs for various functional materials.

The demand for applications, such as coatings, separation filters, and electronic packaging, has greatly driven the research of polymer films. At nanometer scale, mechanical properties of thin polymer films can significantly deviate from bulk. Despite outstanding progresses, there still lack deep discussions on nonlinear viscoelastic–viscoplastic response and their interactions under nanoconfinement. In this work, by conducting measurements via the bubble inflation method and modelling using Schapery and Perzyna equations, we demonstrate nonlinear viscoelastic–viscoplastic properties of freely standing thin polystyrene (PS) films. The results show the unchanged glassy compliance and the rubbery stiffening phenomenon for thin PS films, where the lower rubbery plateau in rubbery stiffening may originate from the induced molecular orientation by plastic deformation. With decreasing film thickness, viscosity and yield stress in viscoplasticity increase in an exponential and a linear trend, respectively, indicating the significant role of nanoconfinement effect on viscoplastic properties. These findings may reveal that there are many properties from linear viscoelasticity to nonlinear viscoelasticity–viscoplasticity that need to be explored and unveiled for sufficient understanding of the nanoconfinement effect on altering mechanical behavior of polymers.

The development of safe and high-performance solid polymer electrolytes (SPEs) is central to advancing solid-state lithium batteries. Recently, there has been an emergence of promising SPEs that combine poly(ethylene oxide) (PEO) with other components in nonlinear architectures including block, bottlebrush, and star structures, yet rational design of nonlinear architectures remains elusive. Here we employ coordination star polymers (CSPs) with precisely defined arm numbers and tunable PEO arm lengths to study the structural factors governing ion transport. Systematic experiments combined with coarse-grained molecular dynamics simulations reveal a universal conductivity maximum at medium arm lengths, arising from a balance between the conformational confinement of short arms and the crystallization tendency of long arms. This behavior aligns well with the Daoud–Cotton model, which describes concentrated and semi-diluted regimes along the polymer arms. The resulting framework establishes transferable design rules that link arm length, arm number, and chain dynamics to ionic conductivity. Beyond CSPs, these principles provide a predictive foundation for engineering star, comb, and bottlebrush electrolytes with optimized performance.