You could also try to fit an ML potential to some expensive method, but it’s very easy to produce very wrong things if you don’t know what you’re doing (I wouldn’t be able for one)
These are potentially tremendously helpful. But in the context of AI x-risk it’s still not enough to be concerning. A force field that gives accurate results 90% of the time would tremendously accelerate experimental efforts. But it wouldn’t be reliable enough to one-shot nanotech as part of a deceptive turn.
You could also try to fit an ML potential to some expensive method, but it’s very easy to produce very wrong things if you don’t know what you’re doing (I wouldn’t be able for one)
It’s coming along quite fast. Here’s the latest on ML-trained molecular dynamics force fields that (supposedly) approach ab initio quality:
https://www.nature.com/articles/s41524-024-01205-w
These are potentially tremendously helpful. But in the context of AI x-risk it’s still not enough to be concerning. A force field that gives accurate results 90% of the time would tremendously accelerate experimental efforts. But it wouldn’t be reliable enough to one-shot nanotech as part of a deceptive turn.