A lot of the public successes with agents is really LLM-driven local search against an objective function that is evaluated in more traditional ways. This one seems to fit the pattern.

Modeling superconductivity with DFT is tricky, there are plenty of DFT reports from reputable groups explaining why LK-99 should be superconducting. It’s a limitation of the theory, DFT can’t model correlated electron states well, and it’s not great at finite temperature, and both of those are important for superconductivity.

I'm under the impression that this kind of modeling is one of the applications that quantum computers are likely to be good at.

I'd imagine there's a lot of documented research which has attempted to find such things using classical computers.

Seems like there would be a lot of well structured context for somebody to use while directing agents to repeat that research, now with updated models once quantum computing is ready for that kind of task.

They ran Quantum Espresso which is ok, but by no means the 'state of the art' for DFT. And in case, any DFT computation has to be taken with a few pounds of grains of salt before getting too excited about it.

No offense to the person writing this (assuming they did at all), but I'm not sure they really understand what they're doing..

not a classic simluation- a quantum simulation. This means they put a lot more work into representing the wave function of the simulation and modelling quantum effects.

Frankly, there is no point in trying to "understand" what an LLM does. Their thought process is effectively undecipherable by humans (it's essentially information arising from information) so even such a "simple explanation" is almost certainly wrong. The agents might appear to have "used this method", but the actual method of computation is far beyond our grasp.

Why are people being so belligerent about this? I thought it's fairly obvious at this point that LLM reasoning is far beyond anyones understanding. Or does anyone have a refutation?