Skip to content
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

Exporting models to use in popular DFT codes #94

Open
jackbaker1001 opened this issue Dec 7, 2023 · 0 comments
Open

Exporting models to use in popular DFT codes #94

jackbaker1001 opened this issue Dec 7, 2023 · 0 comments

Comments

@jackbaker1001
Copy link
Contributor

Grad DFT is not intended as a general purpose high performance DFT code. Its domain of applicability is for training neural functionals. Accordingly, if we wish to perform production level simulations using functionals learnt in Grad DFT, we need to be able to use these functionals in high performance codes.

While learning is performed using Gaussian basis sets (and there will be some biases imparted by this), in principle, functionals are of the density so are basis set agnostic. This means that we can interface learnt models to any DFT code, really.

There are two things to do/decide upon here.

(1) Build a library interface which can be compiled and linked in with standard DFT codes in a similar spirit to Libxc. This could perhaps be an addition to Libxc itself or a a completely standalone library. Likely, we would end up with a different repo for this code than this one.

(2) What is the model export format we should use which is most seamlessly to integrate with the interface?

@jackbaker1001 jackbaker1001 added this to the High priority featutres milestone Dec 7, 2023
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

1 participant