Best resources for someone going from experimental to computational chemistry

Question

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

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:

• Materials Project - in addition to a huge database of computed properties and extracted data, they run a seminar series including on Machine Learning (e.g., "Inverse Design, Why Aren't We There Yet" or "Data Driven Chemical Understanding").

• Open Catalysis Project - "To enable the broader research community to participate in this important project, we have released the Open Catalyst 2020 (OC20) and 2022 (OC22) datasets for training ML models. These datasets altogether contain 1.3 million molecular relaxations with results from over 260 million DFT calculations."

• Open Quantum Materials Database - this focuses on the formation energies (e.g., is a particular material going to be stable).

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!