In a few months, I will complete my PhD and I'm considering leaving experimental chemistry and looking for a postdoc on computational chemistry. During my PhD I used some computational tools like CASTEP and Gaussian, to be honest, I used these tools without understanding a lot of the physics behind them and I guess that should be the step forward.

I would like to keep working on heterogeneous catalysis, but I'm interested in exploring computational approaches like molecular dynamics, machine learning, some data science, and computing observable properties (spectroscopy?)

I do understand that I will be a bit behind other people that did PhDs in computational chemistry, and for that reason, I want to learn as much as I can before I apply for such positions. I did some googling and found a lot of books about computational chemistry, but they are so many that it is difficult to select the one that would make the transition “easy and fast”. A bit like a catalyst?!

With all that said I was wondering if someone could give their opinion about what topics I should focus my study on and provide some resources that cover those same topics. I’m trying to define a path forward that would make this transition efficient and not spend hours learning stuff that is important but not essential

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    $\begingroup$ Welcome to the site! If you haven't yet, check out some of the reference-request and education questions on the site. This should serve as a good starting point for resources and directing your studies. $\endgroup$
    – Tyberius
    Commented Aug 30, 2022 at 13:48
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    $\begingroup$ Have you attended any training workshops for simulations software (e.g. the CASTEP training workshop)? These are good ways to get to know a lot about the methods and practice in a short space of time. $\endgroup$ Commented Aug 31, 2022 at 0:23
  • $\begingroup$ Thanks @Tyberius I was not aware of those resources. It would be great if they appeared on the front page. $\endgroup$
    – manuelpb
    Commented Sep 1, 2022 at 9:59
  • $\begingroup$ @PhilHasnip well that sounds great I will take a look if something is available $\endgroup$
    – manuelpb
    Commented Sep 1, 2022 at 10:00

1 Answer 1


There are advantages for someone who has experience with both experiments and simulations. You don't mention your specific experimental expertise, but looking for topics (or postdocs) that bridge between those experiences could be good (e.g., if you did experimental spectroscopy, exploring groups doing photocatalysis prediction/design).

For solid materials, there are several good resources, for example:

There are several efforts to understand stability and synthetic accessibility, and some experimental background could help significantly with that. Between workshops and tutorials on particular projects (e.g., CASTEP or CP2K, etc.), going through some machine learning tutorials, or attending a workshop for a larger project (e.g., Materials Project) there are several ways to gain more experience with computational efforts.

And of course, there's always MM.SE to ask questions!


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