Of course, none of DFT-raMO would be possible without the contributions of the following:

Original DFT-raMO:
Prof. Danny Fredrickson, concept
Dr. Vincent Yannello, lead and coding
Dr. Yiming Guo, math and coding
Dr. Erdong Lu, extra testing

For the current version of DFT-raMO, my colleague Brandon Flores was invaluable with his development of Electrum.jl (https://github.com/brainandforce/Electrum.jl/) which DFT-raMO relies on, and his knowledge of Julia and Git!

I'm still working on my DFT-raMO.jl package, but I'm excited to get it published and shared with the scientific community so anyone can use it! 🤞 Coming soon 2024...

I'd love to answer any questions you may have about computational solid state chemistry, writing scientific code/scripts, or DFT-raMO if you have any!

In the meantime, I will close with relevant further reading!

The DFT-raMO method: https://pubs.acs.org/doi/10.1021/acs.inorgchem.0c01393
Psphere update to DFT-raMO: https://pubs.acs.org/doi/10.1021/acs.inorgchem.2c03393

The coding process is usually:

idea -> sketch it out -> attempt to code -> test -> debug -> debug -> debug -> debug -> debug ... -> commit change

It's almost similar to writing a manuscript in that aspect. 😅

I find myself coding in short bursts rather than on a daily basis! This is because I have other tasks to do, like analyzing data or writing manuscripts!

But if you catch me on a coding day, my screen might look something like this!

Not all computational chemists write code regularly, but sometimes they write scripts to automate their tasks or process data! These scripts are relatively simple.

When you need a collection of scripts to perform complex tasks, you might consider writing a software package.

.. and this is where we get into one aspect of computational chemistry. Coding and software!

Before I begin, a disclaimer: I haven't taken a single coding class since high school, so a lot is learned on-the-job or self taught! 😱