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Journal Club for November 2021: Machine Learning Potential for Atomistic Simulation

Submitted by Wei Gao on

 

Wei Gao

Department of Mechanical Engineering, University of Texas at San Antonio

 

In this journal club, we provide a brief summary on the concept, recent progress and tools of machine learning (ML) potential for atomistic materials modelling. We hope that it could benefit to the readers who are new to this filed and plan to develop their own or use others ML potentials. Comments and disscussions are welcomed. 

 

Hydrogen damage in FCC steel single crystals

Submitted by Amir Siddiq on

Our recent paper on predicting hydrogen damage in FCC steel single crystals. Most important thing about this paper is that the special issue is dedicated to Professor Siegfried Schmauder on his 65th birthday. Happy birthday Professor Schmauder and thanks for all your guidance, support and collaborations throughout my journey which still continues.

EML Webinar (Season 2) on 4 November 2021 by Hanqing Jiang on origami-based metamaterials: mechanics and devices

Submitted by Teng Li on

 

EML Webinar (Season 2) on 4 November 2021 will be given by Hanqing Jiang on origami-based metamaterials: mechanics and devices. Discussion leader: Pradeep Sharma, University of Houston

Time: 10 am Boston, 3 pm London, 10 pm Beijing on 4 November 2021

Developments in Photoelasticity: A renaissance

Submitted by tarkes on

Have you ever wondered that you can visually see the stress patterns live in a loaded structure! I am not speaking about the contours that you see in commercial finite element software #fea .

For e.g. see the colorful image attached. The fringe patterns are the live stress field around a crack in a loaded structure.

Find more answers on how to interpret these images using state of the art image processing technology in the newly published book:

Improved oxidation resistance of high emissivity coatings on fibrous ceramic for reusable space systems

Submitted by Dr. Hanaor - D… on

Towards the development of reusable space systems, high emissivity coatings on fibrous ceramic substrates with improved thermal resistance are needed. In this study WSi2–MoSi2–Si–SiB6-borosilicate glass coatings were prepared on fibrous ZrO2 by slurry dipping and subsequent high temperature rapid sintering. A coating with 20 wt% WSi2 and 50 wt% MoSi2 presents optimal thermal stability with only 10.06 mg/cm2 mass loss and 4.0% emissivity decrease in the wavelength regime 1.27–1.73 μm after 50 h oxidation at 1773 K. The advantages of double phase metal-silicide coatings combining WSi2 and MoSi2 include improved thermal compatibility with the substrate and an enhanced glass-mediated self-healing ability.

Misuse of Eringen's non-local elasticity theory for functionally graded materials

Submitted by rbatra on

Nearly 50 years ago, Eringen developed a nonlocal theory of elasic solids according to which the Causchy stress tensor "sigma" at a point x depends upon the strain tensor "epsilon" not only at the point x but also at all other points in the body.  For homogeneous solids, and a few additional assumptions, he showed that the constitutive relation can be reduced to a differential form that has become popular among people studying deformations of functionally graded materials (FGMs).  However, an FGM is inhomogene

Statistical mechanics of a dielectric polymer chain in the force ensemble

Submitted by matthew.grasinger on

Dear colleagues,

We invite you to see the preprint of our new paper "Statistical mechanics of a dielectric polymer chain in the force ensemble" that will appear in Journal of the Mechanics and Physics of Solids. Here we compute the electroelasticity of single polymer chains using both analytical approximations and novel MCMC techniques. Working in the fixed force ensemble facilitates the derivation of the analytical approximations, which are shown to agree well with the MCMC results. This work complements prior work on the statistical mechanics of dielectric polymers chains obtained in a different ensemble. (https://doi.org/10.1016/j.jmps.2021.104658).

Machine-learned prediction of the electronic fields in deformed crystals

Submitted by SwarnavaGhosh on

Dear Colleagues,

I am writing to share an article titled, "Machine-learned prediction of the electronic fields in a crystal", co-authored by Ying-Shi Teh, Professor Kaushik Bhattacharya and myself. The article is published in the journal Mechanics of Materials. Link to the article:  https://doi.org/10.1016/j.mechmat.2021.104070