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This sounds like a good way to approach it. If there’s a system that is currently impossible to make decent predictions on then any partially accurate result could be beneficial. I’d be suspicious of someone claiming O(n) on QM however. They’d have to be very simple estimates, not sure how useful they’d be.

To your parents comment, it’d depend heavily on the molecules and systems under study and startup goals.

> The short of it is that literally no business will accept data that is generated this way until someone shows that every neural net model trained in this way produces solutions that are mathematically equivalent to a validated method.

This would seem to be wrong approach this early on...

> At best it might be used as a filter step in some pipeline, but that's not going to have much of an effect, and certainly not something on which to bet the success of a startup.

I don’t think a startup based on providing ‘QM simulation as a service’ would work very well and be fraught with issues. However, many industries could make significant usage of a pipeline filtering possible solutions which could be validated experimentally or with more traditional methods.

IMHO, to work with this a startup would need to be a vertically integrated company solving a specific class of problems (say batteries). Even then the current State of the Art still seems a few years off before I’d want to do a startup in the area, though it’s much closer with the recent results.




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