I have a dataset which has names of compounds and their compositions. Like below
Sulphuric Acid=>[H,S,O] (Hydrogen, sulphur, oxygen) Oxalic Acid=>[H,C,O] Sodium Oxalate=>[Na,C,O] Potassium Sulphate=>[K,S,O] ...
Now I would need to train a model which can tell me Sodium Sulphate as [Na,S,O]. Note that sodium sulphate represents something not in training dataset. I have tried searching for possible ideas but nothing came up. Then I thought it could be hierarchical clustering like
Sodium-Oxalate | | Oxalate Sodium | | | C O Na
But then in hierarchical clustering the base/leafs are different. But here they are shared. Its like a graph. So What machine learning algorithm can help? Any other clustering? NLP/word clustering (if yes How)?
Another approach I could think of is like
Word2Vec where I generate embeddings for each word (C,H,Sodium), all will have embeddings. And based on what is closer to the word I am asking I will give output. But this needs huge amount of data. I only have around 1k common compounds. And the approach won't generalize to any problem like this with less data.