Effective Mechanical Properties of Multilayer Nano-Heterostructures

Two-dimensional and quasi-two-dimensional materials are important nanostructures because of their exciting electronic, optical, thermal, chemical and mechanical properties. However, a single-layer nanomaterial may not possess a particular property adequately, or multiple desired properties simultaneously. Recently a new trend has emerged to develop nano-heterostructures by assembling multiple monolayers of different nanostructures to achieve various tunable desired properties simultaneously. For example, transition metal dichalcogenides such as MoS2 show promising electronic and piezoelectric properties, but their low mechanical strength is a constraint for practical applications. This barrier can be mitigated by considering graphene-MoS2 heterostructure, as graphene possesses strong mechanical properties. We have developed efficient closed-form expressions for the equivalent elastic properties of such multi-layer hexagonal nano-hetrostructures. Based on these physics-based analytical formulae, mechanical properties are investigated for different heterostructures such as graphene-MoS2, graphene-hBN, graphene-stanene and stanene-MoS2. The proposed formulae will enable efficient characterization of mechanical properties in developing a wide range of application-specific nano-heterostructures

Probing the shear modulus of two-dimensional multiplanar nanostructures and heterostructures

Generalized high-fidelity closed-form formulae have been developed to predict the shear modulus of hexagonal graphene-like monolayer nanostructures and nano-heterostructures based on a physically insightful analytical approach. Hexagonal nano-structural forms (top view) are common for nanomaterials with monoplanar (such as graphene and hBN) and multiplanar (such as stanene and MoS2) configurations. However, a single-layer nanomaterial may not possess a particular property adequately, or multiple desired properties simultaneously. Recently, a new trend has emerged to develop nano-heterostructures by assembling multiple monolayers of different nanostructures to achieve various tunable desired properties simultaneously. Shear modulus assumes an important role in characterizing the applicability of different two-dimensional nanomaterials and heterostructures in various nanoelectromechanical systems such as determining the resonance frequency of vibration modes involving torsion, wrinkling and rippling behavior of two-dimensional materials. We have developed mechanics-based closed-form formulae for the shear modulus of monolayer nanostructures and multi-layer nano-heterostructures. New results of shear modulus are presented for different classes of nanostructures (graphene, hBN, stanene and MoS2) and nano-heterostructures (graphene–hBN, graphene–MoS2, graphene–stanene and stanene–MoS2), which are categorized on the basis of fundamental structural configurations. The numerical values of shear modulus are compared with the results from the scientific literature (as available) and separate molecular dynamics simulations, wherein a good agreement is noticed. The proposed analytical expressions will enable the scientific community to efficiently evaluate shear modulus of a wide range of nanostructures and nanoheterostructures

Atomistic simulation assisted error-inclusive Bayesian machine learning for probabilistically unraveling the mechanical properties of solidified metals

Abstract Solidification phenomenon has been an integral part of the manufacturing processes of metals, where the quantification of stochastic variations and manufacturing uncertainties is critically important. Accurate molecular dynamics (MD) simulations of metal solidification and the resulting properties require excessive computational expenses for probabilistic stochastic analyses where thousands of random realizations are necessary. The adoption of inadequate model sizes and time scales in MD simulations leads to inaccuracies in each random realization, causing a large cumulative statistical error in the probabilistic results obtained through Monte Carlo (MC) simulations. In this work, we present a machine learning (ML) approach, as a data-driven surrogate to MD simulations, which only needs a few MD simulations. This efficient yet high-fidelity ML approach enables MC simulations for full-scale probabilistic characterization of solidified metal properties considering stochasticity in influencing factors like temperature and strain rate. Unlike conventional ML models, the proposed hybrid polynomial correlated function expansion here, being a Bayesian ML approach, is data efficient. Further, it can account for the effect of uncertainty in training data by exploiting mean and standard deviation of the MD simulations, which in principle addresses the issue of repeatability in stochastic simulations with low variance. Stochastic numerical results for solidified aluminum are presented here based on complete probabilistic uncertainty quantification of mechanical properties like Young’s modulus, yield strength and ultimate strength, illustrating that the proposed error-inclusive data-driven framework can reasonably predict the properties with a significant level of computational efficiency

Effect of Vacancy Defects on Generalized Stacking Fault Energy of FCC Metals

Molecular dynamics (MD) and density functional theory (DFT) studies were performed to investigate the influence of vacancy defects on generalized stacking fault (GSF) energy of fcc metals. MEAM and EAM potentials were used for MD simulations, and DFT calculations were performed to test the accuracy of different common parameter sets for MEAM and EAM potentials in predicting GSF with different fractions of vacancy defects. Vacancy defects were placed at the stacking fault plane or at nearby atomic layers. The effect of vacancy defects at the stacking fault plane and the plane directly underneath of it was dominant compared to the effect of vacancies at other adjacent planes. The effects of vacancy fraction, the distance between vacancies, and lateral relaxation of atoms on the GSF curves with vacancy defects were investigated. A very similar variation of normalized SFEs with respect to vacancy fractions were observed for Ni and Cu. MEAM potentials qualitatively captured the effect of vacancies on GSF

Effect of Resistance Spot Welding Parameters on Weld Pool Properties in a DP600 Dual-Phase Steel: A Parametric Study using Thermomechanically-Coupled Finite Element Analysis

The objective of this research is to quantify the effects of resistance spot welding (RSW) parameters on different weld properties of a dual-phase steel. A finite element based model was used which accounted for the following required physical interactions: the interaction between (1) the electro-kinetics and heat transfer via the Joule effect, (2) the heat transfer and phase transformations through latent heat, and (3) the heat transfer, electro-kinetics, and mechanical behavior via the contact conditions. The effects of the RSW parameters on weld properties were investigated within a design of experiments framework by altering (1) the electrical current intensity, (2) the welding time, (3) the sheet thickness, (3) the electrode face radius, and (5) the squeeze force at multiple levels. The simulation results were analyzed using the analysis of variance (ANOVA) technique to show the effects of these parameters and their potential interactions, along with their significance. The current intensity was the most influential factor and resulted in an increased size of molten zone and the heat affected zone. The sheet thickness and welding time also showed significant contributions in changing the weld properties. The effects of the other parameters were less significant. The importance of this study is that finding the optimal process window for RSW parameters can help to engineer the desired weld properties

Algorithm Development in Computational Materials Science

Researchers share their views on efforts being made to develop algorithms in computational materials science. The development of solution algorithms will enable the treatment of increasingly complex systems and materials over longer spans of simulated time in an acceptable amount of computational time. The complexity of multiscale and multiphysics models is the key issue, with the goal of improving the representation of the relevant physical and chemical processes being essential. Strategies to achieve this complexity vary from extending existing methods into foreign regimes of length time energy phase space to the coupling of multiple methods, each firmly rooted in its own regime. The hardware and techniques available to the experimental materials scientist have also evolved over time, necessitating algorithms that expand the frontiers of data acquisition and analysis

Transformations and cracks in zirconia films leading to oxidation of zircaloy

This work was aimed to identify the root cause of breakaway oxidation of Zircaloy and the origin of Zircaloy cladding failure. Circumferential compressive stress is highly promoted by tetragonal to monoclinic phase transition. Therefore, this transformation is rather beneficial as it will increase the time to attain critical oxide thickness (at which the stress changes from compressive to tensile), and thus retards breakaway oxidation