Date of Award

2022

Document Type

Open Access Dissertation

Degree Name

Doctor of Philosophy in Mechanical Engineering-Engineering Mechanics (PhD)

Administrative Home Department

Department of Mechanical Engineering-Engineering Mechanics

Advisor 1

Susanta Ghosh

Committee Member 1

Gregory Odegard

Committee Member 2

Shiva Rudraraju

Committee Member 3

Zequn Wang

Abstract

“What could we do with layered structures with just the right layers?” asked Richard Feynman in his famous 1959 lecture, “There’s plenty of room at the bottom.” With the help of the amazing developments of the past several years, we are coming close to answering that question. In 2004, graphene was first isolated from graphite and only six short years later it won the Nobel Prize in Physics. Graphene is one atomic layer of Carbon, it is the thinnest and yet the strongest materials we have ever seen. It is 200 times stronger than its equivalent weight in steel and yet it is extremely flexible. It is an excellent conductor of heat and electricity and it is transparent. More interestingly, Graphene was the first of its clan that we stumbled upon. Since then we have discovered a whole family of 2D materials. Super thin to the physical limit of thinness but with remarkable properties that opens up new horizons for us. Although these 2D materials are very young, many applications have already been developed around them.

Moreover, these properties are coupled with its mechanics and deformation patterns. To obtain the desired property from these 2D materials it is important to deform them in a controlled manner. The existence of computational models have always helped in exploring their deformation and property changes very efficiently and accurately. Despite having such a wide range of applications there exist no computational models that can simulate large scale samples of these materials. There exist continuum models which are efficient but not very accurate, on the other hand there exist pure atomistic models which are very accurate and reliable but require huge computational power to simulate large scale samples. In order to overcome these limitations computational models are developed in the present work, which enables us to simulate these 2D materials under various loading conditions very accurately, reliably and efficiently.

With the help of these models that are developed in this work, we can simulate large-scale samples of these materials and provide more insight into their properties based on the deformation pattern they exhibit. Even though these models are an order of magnitude more efficient than atomistic models while providing the same level of accuracy, they still require high-performance computing facilities. To further improve the efficiency Machine Learning methods are developed and implemented in addition to the developed atomistic-continuum models. we can simulate large scale samples of these materials and provide more insight into their properties based on the deformation pattern they exhibit. Even though these models are an order of magnitude more efficient than atomistic models while providing the same level of accuracy, they still require high performance computing facilities. To further improve the efficiency Machine Learning methods are developed and implemented in addition to the developed atomistic-continuum models.

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