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Computational Study of Mg3Bi2 as Anodes for Mg-ion batteries

Submitted by Dr. Hanaor - D… on
research
Magnesium ion battery
Mg3Bi2
anode

Charging/discharging mechanism in Mg3Bi2 anodes for Mg-ion batteries; The role of the spin-orbit coupling

Magnesum ion batteries are an exciting new technology that offer prospects towards significantly higher capacities of electrochemical storage. During discharge, each magnesium 2+ ion which moves through the electrolyte to the cathode results in 2 electrons driven through an electrical circuit, allowing for potentially higher performance in secondary ion batteries. 

Special Issue "The Physics of Joining and Additive Manufacturing"

Submitted by amin_ebrahimi on
research
welding
additive manufacturing
Materials Science and Engineering
materials processing
Computational Material Science

 

Special Issue "The Physics of Joining and Additive Manufacturing"

Link: https://www.mdpi.com/journal/applsci/special_issues/80839160HW

 

Dear Colleagues,

 

Mechanical couplings of 2D lattices uncovered by decoupled micropolar elasticity tensor and symmetry operation

Submitted by Joshua on
research
mechanical coupling
point groups
symmetry operation
micropolar elasticity
mechanical metamaterials

We introduce a generalized methodology to uncover all mechanical couplings in 2D lattice geometries by obtaining the decoupled micropolar elasticity tensor. We also correlate the mechanical couplings with the point groups of 2D lattices by applying the symmetry operation to the decoupled micropolar elasticity tensor. The decoupled micropolar constitutive equation reveals eight mechanical coupling effects in planar solids, four of which are discovered for the first time in the mechanics' community.

Defect-based Physics-Informed Machine Learning Framework for Fatigue Prediction

Submitted by enrico.salvati1 on
research
Fatigue
machine learning
physics-informed
additive
LEFM
defects

I would like to draw your attention to our recently proposed predictive method based on a semi-empirical model (LEFM) and Neural Network, exploiting the Physics-informed Machine Learning concept. We show how the accuracy of state-of-the-art fatigue predictive models, based on defects present in materials, can be significantly boosted by accounting for additional morphological features via Physics-Informed Machine Learning.

Article: An Atomistic-based Finite Deformation Continuum Membrane Model for Monolayer Transition Metal Dichalcogenides

Submitted by susanta on
research

Highlights

• A finite-deformation crystal-elastic membrane model for TMD monolayers is presented.

• Strains of the middle surface and two normal-stretches describe the deformation.

• The continuum hyperelastic strain energy is obtained from an interatomic potential.

• The present model matches well with the purely atomistic simulations.

Abstract

Temperature-dependent multiscale modeling of graphene sheet under finite deformation

Submitted by M. Jahanshahi on
research

The homogenized constitutive models that have been utilized to simulate the behavior of nanostructures are typically based on the Cauchy–Born hypothesis, which seeks the fundamental properties of material via relating atomistic information to an assumed homogeneous deformation field. It is well known that temperature has a profound effect on the validity and size-dependency of the Cauchy-Born hypothesis in finite deformations.

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