Learning the exchange-correlation functional from nature with fully differentiable density functional theory — mentioned by fock

Mon, 14 Feb 2022 21:55:12 GMT

I don't know if I have missed the big thing here (was supposed to do exactly the same flowery thing described there for crystals around 2019-2020), but the graphic with the Autoencoder is roughly what people did in 2018 (Gomez-Bombarelli, https://pubs.acs.org/doi/10.1021/acscentsci.7b00572), I think the review cited reproduced this. Also notice: it has the MLP in the middle, the performance of which was/is not really helping either - especially if your model should actually produce novel stuff, e.g. extrapolate. Finally: every kid can draw up novel structures. Then: how do you actually…

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Learning the exchange-correlation functional from nature with fully differentiable density functional theory · HackerPapers discussions

Learning the exchange-correlation functional from nature with fully differentiable density functional theory — mentioned by fock