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molecular dynamics

matthew.grasinger's picture

Summer research opportunities in machine learning and computational mechanics at AFRL

The DoD HPC Modernization Program has high-performance computing internship opportunities at the Air Force Research Laboratory. These internships give undergraduate and graduate students the opportunity to perform scientific, computational research alongside AFRL researchers in support of the US Air Force’s mission.

jfmolinari's picture

Journal club for December 2023 : Recent trends in modeling of asperity-level wear

Ernest Rabinowicz’s words, spoken two decades ago in his groundbreaking textbook on the friction and wear of materials [1], continue to resonate today: ’Although wear is an important topic, it has never received the attention it deserves.’ Rabinowicz’s work laid the foundation for contemporary tribology research [2]. Wear, characterized as the removal and deformation of material on a surface due to the mechanical action of another surface, carries significant consequences for the economy, sustainability, and poses health hazards through the emission of small particles. According to some estimates [1, 3], the economic impact is substantial, accounting for approximately 5% of the Gross National Product (GNP).

Despite its paramount importance, scientists and engineers often shy away from wear analysis due to the intricate nature of the underlying processes. Wear is often perceived as a ”dirty” topic, and with good reason. It manifests in various forms, each with its own intricacies, arising from complex chemical and physical processes. These processes unfold at different stages, creating a time-dependent phenomenon influenced by key parameters such as sliding velocity, ambient or local temperature, mechanical loads, and chemical reactions in the presence of foreign atoms or humidity.

The review paper by Vakis et al. [5] provides a broad perspective on the complexity of tribology problems. This complexity has led to numerous isolated studies focusing on specific wear mechanisms or processes. The proliferation of empirical wear models in engineering has resulted in an abundance of model variables and fit coefficients [6], attempting to capture the intricacies of experimental data.

Tribology faces a fundamental challenge due to the multitude of interconnected scales. Surfaces exhibit roughness with asperities occurring at various wavelengths. Only a small fraction of these asperities come into contact, and an even smaller fraction produces wear debris. The reasons behind why, how, and when this occurs are not fully understood. The debris gradually alter the surface profile and interacts with one another, either being evacuated from the contact interface or gripping it, leading to severe wear. Due to this challenge of scales, contributions of numerical studies in wear research over the past decades sum up to less than 1% (see Fig. 1). Yet, exciting opportunities exist for modeling, which we attempt to discuss here.

While analyzing a single asperity contact may not unveil the entire story, it arguably represents the most fundamental level to comprehend wear processes. This blog entry seeks to encapsulate the authors’ perspective on this rapidly evolving topic. Acknowledging its inherent bias, the aim is to spark controversies and discussions that contribute to a vibrant blogosphere on the mechanics of the process.

The subsequent section delves into the authors’ endeavors in modeling adhesive wear at the asperity level. Section 3 navigates the transition to abrasive wear, while Section 4 explores opportunities for upscaling asperity-level mechanisms to the meso-scale, with the aspiration of constructing predictive models. Lastly, although the primary focus of this blog entry is on modeling efforts, it would be remiss not to mention a few recent advances on the experimental front.

vh's picture

3 PhD positions with Freudenberg and our collaborators funded through the Horizon Europe Marie Skłodowska-Curie Actions (MSCA) Doctoral Networks

3 PhD positions funded through the Horizon Europe Marie Skłodowska-Curie Actions (MSCA) Doctoral Networks as part of the “Bridging Models at Different Scales To Design New Generation Fuel Cells for Electrified Mobility (BLESSED)” project.

Markus J. Buehler's picture

MIT Short Course - Predictive Multiscale Materials Design

We at MIT, welcome you to join us this summer, in person, for a hands-on design-to-product Predictive Multiscale Materials Design course from June 13-17, 2022. All course registrants will receive an MIT certificate upon completion.

https://professional.mit.edu/course-catalog/predictive-multiscale-materi...

Nuwan Dewapriya's picture

Molecular‑level investigation on the spallation of polyurea

 

 

 

Our paper "Molecular‑level investigation on the spallation of polyurea" is freely available from this link: https://rdcu.be/cqkbG

We used molecular dynamics (MD) simulations to investigate the nanoscale mechanism associated with the spallation of polyurea, which allowed us to test some assumptions commonly made in the interpretation of similar experiments on the macroscale. 

PhD Position in Multiscale Brain Injury Modelling at KTH-Stockholm

One PhD Student position in Multiscale brain injury modeling for 4-5 years; expected from March 1, 2021 or according to agreement at KTH Royal Institute of Technology in Stockholm, Sweden.

Eligibility: have basic studies at the MSc level in medical engineering, bio mechanics, engineering physics, mechanical engineering, or the like; Requirements for English equivalent to English B/6.

Nuwan Dewapriya's picture

Characterizing fracture stress of defective graphene samples using shallow and deep artificial neural networks

Abstract: Advanced machine learning methods could be useful to obtain novel insights into some challenging nanomechanical problems. In this work, we employed artificial neural networks to predict the fracture stress of defective graphene samples. First, shallow neural networks were used to predict the fracture stress, which depends on the temperature, vacancy concentration, strain rate, and loading direction.

Jingjie Yeo's picture

Conductive Silk‐Based Composites Using Biobased Carbon Materials

Fresh in Advanced Materials! Synthesis & molecular dynamics modeling of conductive, highly stretchable, flexible, & biocompatible silk‐based composite sensors using biobased carbon materials. https://doi.org/10.1002/adma.201904720

Jingjie Yeo's picture

Postdoctoral position in multiscale computational simulations in the J2 Lab for Engineering Living Materials

http://jingjieyeo.github.io/positions.html I am happy to announce that the website of the J2 Lab for Engineering Living Materials is now live! We're very excited to get cracking in Jan 2020 at the Sibley School of Mechanical and Aerospace Engineering in Cornell University, and we're hiring one postdoc experienced in multiscale computational simulations to kickstart our lab. Please visit our website for more details!

Pär Olsson's picture

Available PhD position at the Division of Mechanics at Lund University

The Division of Mechanics at Lund University, Sweden, invites highly motivated and creative applicants for a PhD position oriented towards multiscale modelling of yielding and failure of materials subjected to harsh conditions in energy applications (e.g. components in fusion reactors, battery components, etc.).

Haoran Wang's picture

Modeling Uncertainties in Molecular Dynamics Simulations Using A Stochastic Reduced-Order Basis

We've recently published our new study about Uncertainty Quantification in Molecular Dynamics (MD) Simulations. Due to the selection of functional forms of interatomic potentials or the numerical approximation, MD simulations may predict different material behavior from experiments or other high-fidelity results. In this study, we used Stochastic Reduced Order Modeling (SROM) to achieve

(1) mechanical behavior of graphene predicted by MD simulations in good agreement with the continuum model which has been calibrated by experiments;

Nuwan Dewapriya's picture

Performing Uniaxial Tensile Tests of Graphene in LAMMPS

I would like to share the codes required to perform an end-to-end molecular dynamics simulation, which will be useful to the novice researchers in the filed of atomistic simulations. I focus on simulating uniaxial tensile tests of a graphene sample in the LAMMPS molecular dynamics simulator, and I have attached two MATLAB scripts to create the input files for LAMMPS and to extract data from the LAMMPS output file.

Jingjie Yeo's picture

Carbon nanotube arrays as multilayer transverse flow carbon nanotube membrane for efficient desalination

https://doi.org/10.1016/j.memsci.2019.03.062 This work presents the multilayer transverse flow carbon nanotube (CNT) membrane (TFCM), which resembles vertically aligned CNT arrays, as an alternative candidate for efficient desalination. Using molecular dynamics, this work shows that multilayer TFCM can provide permeability and salt rejection on par with its single layer counterpart.

Kmomeni's picture

PhD Positions in Multiscale Modeling of Materials Under Extreme Conditions

PhD positions are open immediately in Advanced Hierarchical Materials by Design Labat Louisiana Tech Universityon multiscale modeling of materials under extreme conditions. This will be a collaborative project with MITGeorgia Tech, and Idaho National Labthat is supported by Department of Energy.  The candidates must have earned a degree in Mechanical Engineering or related fields (applicants with a MSc will be given priority) and have a solid background in theoretical and computational mechanics.

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