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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.

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  • $\begingroup$ Welcome to DataScienceSE. Can you please clarify what would be the goal of the ML system? What would be given as features and what should it predict? $\endgroup$
    – Erwan
    Jan 28, 2021 at 18:21
  • $\begingroup$ Network will create new formulas according to inout parametres. We will train system with exist formulas. As i explained, formulas have raw materials. For exsmple, if you put a raw material as less or more then result product's properties can change dramatically. So our newtwork will learn impact of raw materials for products. $\endgroup$
    – Coder83
    Jan 29, 2021 at 5:57
  • $\begingroup$ So the features would be the proportions of raw material and from these values the system would predict the properties of the formula, right? $\endgroup$
    – Erwan
    Jan 29, 2021 at 12:11
  • $\begingroup$ @Erwan, exactly right. At the end of day, system will generate new formulas. I will ask the system "create a formula which brightness will be "x", colour will be "y" and viscosity will be "z" then system will create formula or formulas. $\endgroup$
    – Coder83
    Jan 29, 2021 at 16:56
  • $\begingroup$ Well, if you want to be able to ask the system to create a formula based on the properties then you need the opposite: features are properties, predictions are the proportions of raw material. Anyway in both cases you have a fixed-sized vector of numerical values as input and as output, so I suppose that CNN would work (I have no expertise in DL). I'd suggest you start with a baseline system using traditional regression methods, I think SVR (support vector regression) is worth trying. $\endgroup$
    – Erwan
    Jan 29, 2021 at 23:26

1 Answer 1

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When picking an ML method, a great place to start is the sklearn algorithm cheat sheet:

enter image description here

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.

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  • $\begingroup$ Let me know if you have any questions otherwise please accept the answer $\endgroup$
    – WBM
    Mar 14, 2021 at 18:02

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