In a literal sense, of course it doesn’t invalidate it. It just proves that the mathematical limit of accuracy was higher than we thought it was for the particular problem of protein folding. In general, you should not expect two different problems in two different domains to have the same difficulty, without a good reason to (like that they’re solving the same equation on the same scale). Note that Alphafold is extremely extremely impressive, but by no means perfect. We’re talking accuracies of 90%, not 99.9%, similar to DFT. It is an open question as to how much better it can get.
However, the idea that perhaps machine learning techniques can push bandgap modelling further in the same way that alphafold did is a reasonable one. Currently, from my knowledge of the field, it’s not looking likely, although of course that could change . At the last big conference I did see some impressive results for molecular dynamics, but not for atom scale modelling. The professors I have talked to have been fairly dismissive of the idea. I think there’s definitely room for clever, modest improvements, but I don’t think it would change the overall picture.
If I had to guess the difference between the problems I would say I don’t think the equations for protein folding were “known” in quite the way the equations for solving the Schrodinger equation were. We know the exact equation that governs where an electron has to go, but the folding of proteins is an emergent property at a large scale, so I assume they had to work out the “rules” of folding semi-empirically using human heuristics, which is inherently easier to beat.
In a literal sense, of course it doesn’t invalidate it. It just proves that the mathematical limit of accuracy was higher than we thought it was for the particular problem of protein folding. In general, you should not expect two different problems in two different domains to have the same difficulty, without a good reason to (like that they’re solving the same equation on the same scale). Note that Alphafold is extremely extremely impressive, but by no means perfect. We’re talking accuracies of 90%, not 99.9%, similar to DFT. It is an open question as to how much better it can get.
However, the idea that perhaps machine learning techniques can push bandgap modelling further in the same way that alphafold did is a reasonable one. Currently, from my knowledge of the field, it’s not looking likely, although of course that could change . At the last big conference I did see some impressive results for molecular dynamics, but not for atom scale modelling. The professors I have talked to have been fairly dismissive of the idea. I think there’s definitely room for clever, modest improvements, but I don’t think it would change the overall picture.
If I had to guess the difference between the problems I would say I don’t think the equations for protein folding were “known” in quite the way the equations for solving the Schrodinger equation were. We know the exact equation that governs where an electron has to go, but the folding of proteins is an emergent property at a large scale, so I assume they had to work out the “rules” of folding semi-empirically using human heuristics, which is inherently easier to beat.
Do you have a name/link for that conference? I’d be interested in reading those molecular dynamics papers.