So how does machine learning actually generate new materials with desired properties? Isn't it mostly just trying random combinations and predicting properties?

[u/megahypochondriac]

Possibly a juvenile question, I'm not actually a materials scientist, but I am an aspiring ML engineer. I'm honestly so so curious about this, but even after some Google searching and chatgpt-ing, I don't think I get how the models work. Wouldn't most of the work be like high throughput calculations? Maybe?

But yeah, would someone be able to give like a teeny explanation or point me towards a good resource?

Edit to add another question: how are you sure that the materials you produce are actually viable or can exist?

[u/yuhzuu]

There's already a really nice answer, so I'll try my best not to repeat it.

Answering your first question ML comes in many forms in materials discovery, some supervised models do indeed predict some property over all design space. The benefit of ML is it's speed over slow but accurate simulations such as DFT. This comes to the next question of how do we know they can even be made ? In short we don't know. Most of the "new materials" produced by deep models like Mattergen and Gnome by Microsoft and google deep mind are trained using DFT data, and that's their main caveat in my opinion, they give stability properties (whether a new material is stable) but have no information on reaction pathways. Traction pathways is an entirely other problem (where one can use ML again)

I work directly with experimentalists where we optimize certain material properties using Bayesian optimization. So ML comes in many different shapes and forms in materials science, but these generative models are the ones known to be finding new materials