The short answer, I guess, is yes. In theory, you could have a massive, insane network that could take as input any high school science textbook question and output an answer. But think about what that entails:
Take in a question, in any of the various languages that high school science textbooks can be written in. Get all the relevant information out of that. This includes questions like "List four noble gases" as well as ones about applying Kirchoff's laws to a circuit as well as explaining how the ribosome changes its shape to produce proteins. There is soooooooo much information that is contained in just the wording of those questions and so many topics. Then, let's say you have converted the question into a representation that can be "understood" by the algorithm. I want to emphasize that ML models do not "know", "understand", "think", or any other of the words we use to personify them. They are basically just a mathematical function of some level of complexity (usually very complex). That being said, now the algorithm needs to relate this tractable representation of the question to the correct answer.
Since everything before this created some representation of the question, you could view that as a function mapping the input to this representation. Then, everything between intermediate representation and output answer is another function. So, your model needs to learn a single function that is the sum of all human high school level science understanding.
These algorithms you want to build would end up being staggeringly complex. In addition, as you add complexity to an ML model, you need significantly more data to train it. How will you take every high school science book in the world and convert it into digital pairs of questions and answers? How will you handle that some of the answers are incorrect? What is the benefit of this system?
The long answer, I think, is no. It makes no sense to do this. Instead of learning this complex mapping, just build some solvers with equations built in that the user inputs some numbers to and selects the equations. It will take so much less time to build and deploy and I guarantee work better. There are people building ML systems that are trying to get the SAT entirely right (see page 33 of this report). First, I don't think this is what you're asking and second, it still makes more sense, in my mind, to build some solvers. Hopefully, this all makes sense and, if anyone sees things I misstated, let me know.