I will be starting with my PhD in Materials Science and Engineering, in Fall 2020.
I have done M.Sc. in Chemistry (focusing more on Inorganic).
I have worked at a Material Chemistry Lab before and have a paper focusing on optoelectronics.
Also, I don’t know if it gives any insight, I did C++ in school for 2 years. So I understand the basic approach towards a coding problem.
I started with Python and have even tried solving LeetCode questions, could do easy ones with pretty good rating.
I have done courses on ML and Data Science. So I understand the basics of ML, but all theoretical. I do not have any practical experience on it, though I understand the libraries and their commands (Scipy, sklearn, NumPy, Pandas). I understand Regression, Classification and Clustering (again, very basic).
I have read the review article and few of the papers published by the professor of my interest.
- How much ML am I supposed to know before I start with my PhD, or before mailing the professor so that I leave a good impression.
- Is DFT an important part of this field? Since I see this coming up a lot in the papers.
- How much Math background should I have?
- Am I supposed to know Quantum Chemistry as well?