>>> Combining a series of new microscopy techniques, the group present a complete picture of the nanoscale chemical, structural and optoelectronic landscape of these materials
It's not even relevant to call it "microscopy" anymore, we require a new term. It's a complete thin film atlas of all interacting forces of nature. Better data for the models, means higher fidelity simulations.
The question is can AI predict new materials? Can a simulation be sophisticated enough to predict say, high temperature superconductivity in rare earth cuprate perovskites?
If we are going to speculate, then the question is, why couldn't AI do it? What kind of fundamental limitation would it hit? Data? But we could get a ton of data from already existing software[^1]. It is slow, but I have the feeling it wouldn't require as much computing as GPT-3, and it would perhaps be enough to train more efficient neural networks that can do the actual search.
Because how important is for human life, a compiler industry that finds ways to translate complicated simulations to AI algorithms could be the next big thing.
It's not even relevant to call it "microscopy" anymore, we require a new term. It's a complete thin film atlas of all interacting forces of nature. Better data for the models, means higher fidelity simulations.
The question is can AI predict new materials? Can a simulation be sophisticated enough to predict say, high temperature superconductivity in rare earth cuprate perovskites?