This title has to be one of the most genericized-press-release titles posted on HN. Almost every other experimental chemistry paper would technically fit this title! Makes you feel for the copy writer trying to write a title for some complex chemistry paper during the holidays. Despite the bland press release title, it’s a pretty interesting experimental chemistry research and the original paper reads well. It’s surprising how clear the (apparent) transition peaks are in the graph data. These reactions occur on such incredibly minuscule timescales it’s amazing to see it so clearly!
Interesting:
> The PES is fit using the permutation invariant polynomial–neural network (PIP-NN) method [49].
This caught my eye as I recall reading some earlier attempts at NN based polynomial fitting in QM models, but this sounds more advanced. Does anyone know if generalized neural net methods for fitting the potential energy curves are common in QM modeling now? If so, are there limitations to the method that’d prevent adopting it for fitting polynomials in other applications?
Interesting:
> The PES is fit using the permutation invariant polynomial–neural network (PIP-NN) method [49].
This caught my eye as I recall reading some earlier attempts at NN based polynomial fitting in QM models, but this sounds more advanced. Does anyone know if generalized neural net methods for fitting the potential energy curves are common in QM modeling now? If so, are there limitations to the method that’d prevent adopting it for fitting polynomials in other applications?