Back to school: Dirac's student

 I decided to change the name of the blog from "Equation a day keeps from going astray" to "Dirac's student". So why did I do this? Well, I felt the old name was tied to what I originally set out to do which was to create blogs of mathematical refreshers, and that slowly became less and less of my interest. In the past 2 years, of which I wrote only a few blogs, I've mostly focused on science topics or my opinions that are of interest to me.  This has led me down the path of becoming more of a student and trying to learn new topics and express my understanding of ideas. The thing to note is that I'm doing most of this on my personal time outside work and by myself. Thus, I thought the name "Dirac's Student" made more sense, since the great physicist Paul Dirac was considered by many to be a brilliant loner. I particularly marvel at Dirac's capabilities and contributions to quantum theory that explained the nature of electrons/fermions. So sans "brilliant", I will set out to be like Prof. Dirac, wish me luck.

P.S. I was going to go with the name "Feynman's Student" since he is another great physicist that I admire, but ultimately thought it would be easier to be like Dirac than Feynman given how amazing Feynman was at explaining difficult concepts and how to think about science. Dirac was not known for being a good communicator or teacher.

 

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Thank you Prof. KohnGPT, can you ...

As I go into the new year I've decided that I'm going to try to make more of an effort to write more blogs on my thinking. I'm also not going to try and spend a great deal polishing and formatting, rather will publish them and then update them as needed. The point is not so much for the prospective readers but for me to archive and flush out my ideas on several topics that I have been thinking about for the last year. For one I really want to understand more in-depth the design and concepts behind ChatGPT, and more specifically how transformers are used and work. I want to think about how these natural language processing models can be used to improve working scientist writing and computing tasks. More specifically, how could one retrain/teach ChatGPT (assuming it's eventually open-sourced!) to provide materials scientists with template computing workflows. Say I wanted to perform a ground-state DFT calculation using VASP for some magnetic sulfide. I know from experience how to setup the INCAR, POTCAR, KPOINT, and POSCAR files, but how cool would it be to just type into the ChatGPT box:

Create the VASP input files to calculate the ground-state of FeS. Use the known stable form of FeS for the POSCAR file and use the a functional and pseudopotentials that have been used by previous studies.

On top of that imagine that you could then ask,

Well, I actually don't have access to VASP at the moment, can you show me the equivalent input scripts for Abinit or QuantumEspresso?

if this modified ChatGPT is delivered on this, 🤯. Think about a domain large language model for quantum chemistry and DFT, say KohnGPT. I mean imagine all the time saved on redundant tasks by computational scientists and being able to go from one code to another, unbelievable. Granted it is important to know what the keywords and setup correspond to in terms of the physics being simulated, so this isn't a replacement for learning those. But wow would this be cool if you ask me. 

Just so you know I tried this out on ChatGPT, and it failed pretty badly. It gets a good amount write, but it fails or just makes things up (I guess this is called hallucination in NLP). You can query ChatGPT to write some setups if your looking for suggestions, but truthfully any experience DFT practitioner would know these pretty quickly. Here are the outputs* from ChatGPT using the quotes above:

which gives a cubic structure, which is wrong based on the materials project entry for FeS and what ChatGPT says when I asked it "what is the spacegroup number for this structure", for which it says Pnma, #62. When I ask for the INCAR, I get:

It is incomplete and erroneous. The KPOINT and POTCAR request is given as:

The KPOINT file doesn't look bad, but as expected the POTCAR file is pointless. The latter makes sense since the pseudopotential files for VASP are only available for license holders. If I try and ask it to show the corresponding files for Quantum Espresso, it actually doesn't look too bad, but I'm not a heavy user of QE so I would need to verify.

Finally, I'm changing the blog name from "An equation a day keeps from going astray" and may migrate to a platform where I can write the post in Markdown markup.

* I had to ask ChatGPT to explicitly create the input files, it just gave me the steps initially

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New Commentary E-Journal on Comp. Mat. Sci.

The other day I came across a new electronic journal aimed at providing expert opinion and commentary on important papers within the field of computational materials science. The name of the electronic publication is called KIM REVIEW and has the following aim and scope:

"... publishes commentaries on important articles related to classical molecular simulations of hard and soft matter materials. The objectives are to inform practitioners in the field of key contributions, both new developments and foundational work, that they should be aware of, and to provide a forum for community discussion of such innovations."

If this venture proves to be fruitful and gets traction, I think it's going to greatly aid newcomers to the field. This is particularly the case in computational materials science and materials informatics where the explosion of research papers and new tools is far too difficult to keep up with. The one thing though is this is just an opinion/commentary journal so it won't provide the "how-to" part of a research paper. 

The one thing I'm curious about is whether or not the editors of the journal will get enough contributing authors to make the electronic publication be productive. I would guess that only senior, well-established, researchers are going to be willing to contribute given that if their commentary indicates that a paper is "not that impactful" it may be seen as "risky" for a young researcher. The solution is probably just to focus on papers that are known to advance the field of computational materials science and then just focus on providing "spark" notes/commentary. 

Overall, I hope this electronic publication takes off as it will make it much easier to figure out what papers I should read to stay up-to-date. If you are interested,  you can submit recommendations for articles to be reviewed here. Best of luck to the editors and future authors.

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