Evolutionary Search and Theoretical Study of Silicene Grain Boundaries' Mechanical Properties.

Defects such as grain boundaries (GBs) are almost inevitable during the synthesis process of 2D materials. To take advantage of the fascinating properties of 2D materials, understanding the nature and impact of various GB structures on pristine 2D sheets is crucial. In this work, using an evolutionary algorithm search, we predict a wide variety of silicene GB structures with very different atomic structures compared with those found in graphene or hexagonal boron-nitride. Twenty-one GBs with the lowest energy were validated by density functional theory (DFT), a majority of which were previously unreported to our best knowledge. Based on the diversity of the GB predictions, we found that the formation energy and mechanical properties can be dramatically altered by adatom positions within a GB and certain types of atomic structures, such as four-atom rings. To study the mechanical behavior of these GBs, we apply strain to the GB structures stepwise and use DFT calculations to investigate the mechanical properties of 9 representative structures. It is observed that GB structures based on pentagon-heptagon pairs are likely to have similar or higher in-plane stiffness and strength compared to the zigzag orientation of pristine silicene. However, an adatom located at the hollow site of a heptagon ring can significantly deteriorate the mechanical strength. For all of the structures, the in-plane stiffness and strength were found to decrease with increasing formation energy. For the failure behavior of GB structures, it was found that GB structures based on pentagon-heptagon pairs have failure behavior similar to that of graphene. We also found that the GB structures with atoms positioned outside of the 2D plane tend to experience phase transitions before failure. Utilizing the evolutionary algorithm, we locate diverse silicene GBs and obtain useful information about their mechanical properties.

Graph Neural Network Guided Evolutionary Search of Grain Boundaries in 2D Materials.

Grain boundaries (GBs) in two-dimensional (2D) materials are known to dramatically impact material properties ranging from the physical, chemical, mechanical, electronic, and optical, to name a few. Predicting a range of physically realistic GB structures for 2D materials is critical to exercising control over their properties. This, however, is nontrivial given the vast structural and configurational (defect) search space between lateral 2D sheets with varying misfits. Here, in a departure from traditional evolutionary search methods, we introduce a workflow that combines the Graph Neural Network (GNN) and an evolutionary algorithm for the discovery and design of novel 2D lateral interfaces. We use a representative 2D material, blue phosphorene (BP), and identify 2D GB structures to test the efficacy of our GNN model. The GNN was trained with a computationally inexpensive machine learning bond order potential (Tersoff formalism) and density functional theory (DFT). Systematic downsampling of the training data sets indicates that our model can predict structural energy under 0.5% mean absolute error with sparse (<2000) DFT generated energy labels for training. We further couple the GNN model with a multiobjective genetic algorithm (MOGA) and demonstrate strong accuracy in the ability of the GNN to predict GBs. Our method is generalizable, is material agnostic, and is anticipated to accelerate the discovery of 2D GB structures.

An in situ investigation of electromigration in Cu nanowires.

Electromigration in copper (Cu) nanowires deposited by electron beam evaporation has been investigated using both resistance measurement and the in situ scanning electron microscopy technique. During electromigration, voids formed at the cathode end while hillocks (or extrusions) grew close to the anode end. The failure lifetimes were measured for various applied current densities and the mean temperature in the wire was estimated. Electromigration activation energies of 1.06 eV and 0.94 eV were found for the wire widths of 90 nm and 141 nm, respectively. These results suggest that the mass transport of Cu during electromigration mainly occurs along the wire surfaces. Further investigations of the Auger electron spectrum show that both Cu atoms and the surface contaminants of carbon and oxygen migrate from cathode to anode under the electrical stressing.

Surface effect on the elastic behavior of static bending nanowires.

The surface effect from surface stress and surface elasticity on the elastic behavior of nanowires in static bending is incorporated into Euler-Bernoulli beam theory via the Young-Laplace equation. Explicit solutions are presented to study the dependence of the surface effect on the overall Young's modulus of nanowires for three different boundary conditions: cantilever, simply supported, and fixed-fixed. The solutions indicate that the cantilever nanowires behave as softer materials when deflected while the other structures behave like stiffer materials as the nanowire cross-sectional size decreases for positive surface stresses. These solutions agree with size dependent nanowire overall Young's moduli observed from static bending tests by other researchers. This study also discusses possible reasons for variations of nanowire overall Young's moduli observed.

Bulk-wave and guided-wave photoacoustic evaluation of the mechanical properties of aluminum/silicon nitride double-layer thin films.

The development of devices made of micro- and nano-structured thin film materials has resulted in the need for advanced measurement techniques to characterize their mechanical properties. Photoacoustic techniques, which use pulsed laser irradiation to nondestructively induce very high frequency ultrasound in a test object via rapid thermal expansion, are suitable for nondestructive and non-contact evaluation of thin films. In this paper, we compare two photoacoustic techniques to characterize the mechanical parameters of edge-supported aluminum and silicon nitride double-layer thin films. The elastic properties and residual stresses in such films affect their mechanical performance. In a first set of experiments, a femtosecond transient pump-probe technique is used to investigate the Young's moduli of the aluminum and silicon nitride layers by launching ultra-high frequency bulk acoustic waves in the films. The measured transient signals are compared with simulated transient thermoelastic signals in multi-layer structures, and the elastic moduli are determined. Independent pump-probe tests on silicon substrate-supported region and unsupported region are in good agreement. In a second set of experiments, dispersion curves of the A(0) mode of the Lamb waves that propagate along the unsupported films are measured using a broadband photoacoustic guided-wave method. The residual stresses and flexural rigidities for the same set of double-layer membranes are determined from these dispersion curves. Comparisons of the results obtained by the two photoacoustic techniques are made and discussed.