-
Notifications
You must be signed in to change notification settings - Fork 19
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Active learning with unparametrized ACE potential #72
Comments
Hi,
Here is a rough scheme of the procedure. I used this to explain the procedure to another group, but your variation is also correct:
In this scheme, updating the potential to get Best, |
Thank you so much for this great explanation! |
Hello,
In the beginning of fitting using "pacemaker" a "target_potential.yaml" file is generated which is an unparametrized ACE potential, but has the same hyperparameters as the finally fitted potential. Does it make sense to use that potential for active sampling using "pace_activeset"? I guess for active sampling with D-optimality, only the ACE features are required which must be accessible from "target_potential.yaml"
I'm trying to get a representative set of structures from a dataset of 10,000 structures. Initially I randomly select 100 structures, make an "input.yaml" file with the desired hyperparameters, then run "pyace" with the flag "--no-fit" so I get the "target_potential.yaml". Then use "pace_activeset" using the initial 100 random structures and the "target_potential.yaml", to get the active set, then loop over the whole data set using a python code that includes "calc.results['gamma']" of the ACE calculator in ASE to get the extrapolation grade of the structures. and continue this procedure until I get to roughly around 1000 structures that represent the whole data set.
Does this make sense? I'm using "target_potential.yaml" instead of fitting a whole new potential at each active learning cycle, to accelerate the whole process.
Thanks
The text was updated successfully, but these errors were encountered: