Machine Learning Method For Creating Chemical Formula We work at chemical company . We have nearly 3000 chemical formulas which is composed with chemical raw materials.
Our chemical formulas is composed of 20-25 raw materials. As you guess, amount of these raw materials are very important for our formulas. As a result we obtain different products via using different raw materials and different amount of raw materials.
Here is some examples:
Sample Formula 1:
%10 Raw material A
%25 Raw material B
%5 Raw material C
%60 Raw material D

Result product has such properties :
Property X: 3
Property Y: 10
Property Z: 2

What kind of machine learning method or methods should we use? I have looked for CNN but as you see there are many input and output properties as parameters.
Our goal is training a network via using our exist formulas then we will want that our network will create new formulas according to input parameters which we will give.
When picking an ML method, a great place to start is the sklearn algorithm cheat sheet:

You are predicting quantities, so regression class of variables is what you're looking for. Specifically you have multiple continuous inputs and multiple continuous outputs, which is a multivariate (linear) regression family.
As highlighted in the comments, start with a simple baseline model and build up in complexity to attempt to improve performance, towards CNNs or other.