> THE MATERIALS OF THE FUTURE ALREADY EXIST IN THE LAB
Do they? There’s plenty of stuff in our labs, most of them are completely useless, some that were bought to be useless become fashionable again, and we get new and exciting ones every day. There are a lot of issues in going from concept to useable material, and "scaling up" is only one of them.
> FRONTIER INTELLIGENCE WILL BRING THEM TO THE WORLD.
It will probably help, but I doubt it will do it by itself.
> Put simply, materials innovation has a scale-up problem, not a discovery problem.
I just don’t think that’s true. It’s also a scale-up problem, but discovery itself is not solved.
The problem spaces keep getting larger (composites! nanostructures! High-entropy!). High-throughput thermodynamic and electronic structure calculations, automated characterisation and testing, and things like that are being developed because we just don’t know what materials could exist and what could be their properties. The problem is that while there is room for AI there, particularly in automation, even cutting edge models are very dodgy to extrapolate materials properties outside their training sets, which are utterly negligible compared to the size of the search space.
> The bottleneck has never been a shortage of promising candidate materials. It is the decades of trial and error it takes to manufacture even one of them reliably.
It’s worse than that. The first sentence is true (ideas are cheap), but the main bottleneck is to try to figure out the properties of the damn thing and whether some of them are deal breakers or not. The vast majority of materials we come up with never see any application, not because we don’t have processes at the right scale, but because they just have terrible properties.
> THE MATERIALS OF THE FUTURE ALREADY EXIST IN THE LAB
Do they? There’s plenty of stuff in our labs, most of them are completely useless, some that were bought to be useless become fashionable again, and we get new and exciting ones every day. There are a lot of issues in going from concept to useable material, and "scaling up" is only one of them.
> FRONTIER INTELLIGENCE WILL BRING THEM TO THE WORLD.
It will probably help, but I doubt it will do it by itself.
> Put simply, materials innovation has a scale-up problem, not a discovery problem.
I just don’t think that’s true. It’s also a scale-up problem, but discovery itself is not solved.
The problem spaces keep getting larger (composites! nanostructures! High-entropy!). High-throughput thermodynamic and electronic structure calculations, automated characterisation and testing, and things like that are being developed because we just don’t know what materials could exist and what could be their properties. The problem is that while there is room for AI there, particularly in automation, even cutting edge models are very dodgy to extrapolate materials properties outside their training sets, which are utterly negligible compared to the size of the search space.
> The bottleneck has never been a shortage of promising candidate materials. It is the decades of trial and error it takes to manufacture even one of them reliably.
It’s worse than that. The first sentence is true (ideas are cheap), but the main bottleneck is to try to figure out the properties of the damn thing and whether some of them are deal breakers or not. The vast majority of materials we come up with never see any application, not because we don’t have processes at the right scale, but because they just have terrible properties.
In software, AI seems to have inverted the equation.
Building is cheap. Distribution, differentiation and discovering unmet demand are becoming the expensive parts.
also, append `?tune` for fun times!