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I've seen this concept of using LLM/AI/etc for high throughput discovery of materials so, so often in the past 5 or so years and yet there hasn't really been any impact as a result.

I think this is the first one that has actually taken the pain to say how many of the discovered materials are actually feasible which is a real step in the right direction. Probably worth keeping in mind the step beyond plausible synthesis which is the actual cost/effort of the material. There's not much point if you find out RuO2 would be better than SiO2, as an example, if Ru is orders of magnitude more expensive.

A challenge I think you'll run into is that I expect the biggest companies (e.g. IBM) will already be doing the part they need themselves. I heard tell of IBM in particular using ML to improve their own chips before LLMs came along, so I'd be shocked if these bigger companies weren't already doing this for their own problems. Also, if you aren't doing the experiments yourself, it's always going to be a challenge to find a partner to test things for you and this will probably be the major time sink.

good points. one of the reasons we picked the semiconductor industry is that its less price sensitive than others-companies are willing to pay if the performance is there. Effort is a different story though, and definitely a tradeoff to keep in mind. We're doing experiments ourselves now at university partner labs (UC Berkeley and Stanford), which helps us get moving quickly. At some point, we'll need a partner though - the equipment and testing process quickly get very expensive.
Semiconductors are also incredibly risk averse, and requires fairly large gains to be worth the risk.

There are domains (or, mems, photonic sensing, high power) where innovation has a bit more flexibility for the early going.

that's true, we've seen examples of many promising startups that are a few years in and stuck because the industry is so risk averse. will explore the other domains you've mentioned!