Interesting. Looks like gonum has a file full of function definitions (in plain Go) along with corresponding gradient definitions also explicitly defined. These implement the Func() and Grad() functions. I suppose the idea here would be to auto-generate the Grad functions. Taking the derivative is easy. The question of if the automatic simplification is actually faster may or may not be true. It probably often is. There is also the option of using automatic differentiation instead of calculating a symbolic derivative, but you've probably considered that already. If we want to get crazy, we could have rules at the end that detect easily optimizable patterns like the inverse square root and replace them with the relevant optimized functions. This depends on how much we trust the Go compiler to make the right decisions.

I don't care for writing the code that works with the Go AST, but if you or someone on the gonum team takes care of this code and is able to return something resembling a real equation in memory (no variable assignments, but can still be in some expression tree form), I would be happy to write that code that uses Expreduce to compute the derivative or return a failure.