There is a company called Niron Magnetics based out of Minnesota that looks promising. They are making an Iron Nitride magnet that supposedly is better than neodymium. Since it's made out of iron and nitrogen it should also be a lot cheaper to produce.
It’s just a brief counterexample aiming to challenge the parent’s argument that the relative cost of a material is determined by the relative abundance of its constituent elements.
in plain language: they've made a statistical model that predicts curie temperature based on a few data points; the researchers justly called it ML, the hype hunrgy copywriters called it AI (it helps to lie a little to get more clicks and show more ads). Edit: it would be AI if that model, after a day long analysis of the raw data, came up with a new principle explaining this data, and wrote down a formula with some explanations.
Linear and logistic regression are also AI. Language models do not come up with new principles and ideas but they're still called AI. The AI these days is just a marketing term for "we took some data and fit a curve to it".
In this case I think the application of optimization to predict material properties is a very good use case for AI. AI after all is basically regression scaled up with lots of variables and large data sets. Anything with well defined inputs and outputs should be using "AI" to reduce human effort in uncovering statistical patterns that can accelerate research and development. Material science and drug chemistry are obvious use cases. Compiler optimization is another one but I haven't yet seen any real effort for optimizing software development with AI other than copilots for code completion.
ML is a discipline, like engineering, and AI is a product with certain characteristics, like a spaceship. Our engineering isn't good enough yet to build a spaceship.
There are no obstructions to doing that. It is indeed possible to predict properties of superconductors with AI. It's just another regression problem with well defined inputs and outputs. Inputs are the materials and electromagnetic properties of the materials and the output is prediction of superconductivity.
Surprising there is no YC company already working on this. Instead of yet another email company you'd think there would be at least one trying to accelerate R&D for material science and chemistry with AI.
Afaik the fundamental mechanisms governing superconductivity are not known, which means any machine learning guess can only tell you which materials would be statistically likely candidates.
At least the physicists I heard talking about that problem don't see any AI inventing a superconductor directly. Maybe it can help discovering the fundamental mechanisms tho.
