DFT modelling of graphene, fullerenes, nanotubes etc

In summary, this person is looking for advice on how to model graphitic materials using DFT-based software. They are not sure if they need to get into more detail yet, but they are interested in papers that have used the technique to calculate ion sizes. They are looking for a cheaper and simpler program to use.
  • #1
mic*
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Hi all. Does anyone here have any experience modelling graphitic materials using DFT based software (eg VASP)?

I am looking to utilise some simulation results in conjunction with applied research on these materials. The focus is really the applied stuff, and I have very little background in comp-chem.

I was hoping I might be able to get some advice and/or ideas on where to start with software, associated libraries, and other resources.

Cheers.
 
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  • #2
Let me put this another way. I am familiar with the process of literature searching and referencing, but I have no idea what sort of published libraries exist for molecular modelling, or even if there is such a thing, beyond the literature.

If there is such a thing, or anything at all like it, could someone point me in the right direction. I want to sift through existing graphitic models.

Please excuse me if this all seems naive, but I don't have a chemistry background.
 
  • #3
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  • #4
Thank you TeethWhitener, this is exactly what I meant.

Obviously I didn't hit the right search string in my first couple of attempts looking into this and I just got "asky"...

I did search the forum a bit and noticed you reply on a few dft threads. Do you have any preferences, or advice on how they differentiate from each other?

Eg one might be prefererred for those modelling elemental or small compound molecules, another for crystalline solids?
 
  • #5
mic* said:
Thank you TeethWhitener, this is exactly what I meant.

Obviously I didn't hit the right search string in my first couple of attempts looking into this and I just got "asky"...

I did search the forum a bit and noticed you reply on a few dft threads. Do you have any preferences, or advice on how they differentiate from each other?

Eg one might be prefererred for those modelling elemental or small compound molecules, another for crystalline solids?
This is pretty vague. Do you mean a preference for certain programs? Or certain methodologies? Something else?

As far as programs go, I stick to the simple rule: whatever's the cheapest (read: free) and requires the least amount of my time to maintain and keep running on my computer. Abinit and Quantum Espresso are pretty good for materials (I've never used VASP or SIESTA). For molecules (electronic structure), I usually use ORCA but I've used a bunch of different programs in the past (GAMESS, Gaussian, Turbomole, NWChem) and they each have their strengths and weaknesses. For molecular dynamics, I've only ever used NAMD, which I really like, but I don't really do extensive MD calculations.
 
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  • #6
Apologies for vagueness. Despite it, your response is pretty on point. Gives me a bit of research direction. I can't really ask anything more specific until I have a bit of familiarity with abovementioned software and their descriptions etc.

I am not 100% sure how much I need to get into it all yet. My interest relates to papers where dft has been used to calculate approximate ion sizes in various solutions, in various porous carbons and graphitic materials. I just want to understand what has been done and feel capable that I could replicate the published work, if necessary. I need to go back and review the literature again because a lot of the parts relating to dft went straight over my head, can't even be sure what software or correlation functionals were reported, off the cuff...

Having gained a basic understanding of dft and now, from you, a few program names, database locations etc I have plenty to work with.

Cheers.
 

1. What is DFT modelling and how is it used in studying graphene, fullerenes, and nanotubes?

DFT (Density Functional Theory) modelling is a computational method used to investigate the electronic structure and properties of materials. In the case of graphene, fullerenes, and nanotubes, DFT modelling can be used to predict their electronic and mechanical properties, such as band structure, bonding, and stability. This allows researchers to better understand the behavior and potential applications of these materials.

2. What are the main advantages of using DFT modelling for these materials?

One of the main advantages of DFT modelling is its ability to accurately predict the properties of materials at the atomic scale. This is especially useful for studying graphene, fullerenes, and nanotubes, which have unique structures and properties at the nanoscale. Additionally, DFT modelling is relatively fast and cost-effective compared to experimental methods, making it a valuable tool for researchers.

3. What are some of the limitations of DFT modelling for these materials?

While DFT modelling is a powerful tool, it does have some limitations. One major limitation is the choice of exchange-correlation functional, which can affect the accuracy of the results. Additionally, DFT calculations may not capture the full complexity of the materials, such as the effects of defects or interactions with the environment. Therefore, experimental validation is important in order to fully understand the properties of these materials.

4. Can DFT modelling be used to study the growth and synthesis of these materials?

Yes, DFT modelling can be used to study the growth and synthesis of graphene, fullerenes, and nanotubes. By simulating the interactions between atoms or molecules, DFT can provide insights into the mechanisms and energetics of the growth process. This can aid in the development of new synthetic methods and the optimization of existing ones.

5. How is DFT modelling being used to explore the potential applications of these materials?

DFT modelling has been used to investigate the potential applications of graphene, fullerenes, and nanotubes in various fields, such as electronics, energy storage, and biomedical applications. By predicting the properties of these materials, researchers can identify their potential uses and design new devices or systems that take advantage of their unique properties.

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