Am I supposed to know Machine Learning before I start my PhD in Machine Learning in Materials Science I will be starting with my PhD in Materials Science and Engineering, in Fall 2020.
My Background
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.
Present Situation
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.
Questions

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?

Thanks
Short Answer: No.
Long Answer: PhD in multidisciplinary topic expect that you know at least one discipline well. Then you get training in second discipline before applying the knowledge and ideas from both disciplines in order to fulfill PhD requirements.
My personal experience PhD. in biomedical informatics - I graduated in informatics, but nobody expects I have previous knowledge of medicine. (Of course - interest is expected or some courses is a plus). I did  some courses to gain the missing knowledge about medicine after (not full medical curicullum, but some physiology, patophysiology, basic anatomy, biochemistry ...). Thus, I think the same applies to your intention to pursue ML in Material Science - you can get courses of what you miss, supervisor may help you to direct what you need.