I am new here new. I start my experience in ML in material science,
I deal with ceramic refractory materials - material resistant to high temperatures, mostly applied in industrial high-temperature devices. Currently, I am working on a project concerning the development of a new generation of non-chrome, eco-friendly, intelligent refractory materials for the copper industry. I am planning to involve Machine Learning technology to accelerate and optimize the design process. During searching the Internet your work was greatly knowledgeable for my idea.
My project succeeded to pass to the next stage. If I manage to get it, I plan to work on ML.
In this project I will be looking for correlation in relation:
starting composition (input) - final composition of the material (output).
The input and output data will be a qualitative and quantitative analysis of XRD patterns of materials before and after synthesis.
I am wondering which algorithm group my fit my conundrum?
If you can give me any tips I would be very grateful.