Libnxc DocumentationΒΆ
Libnxc is a libary to use machine learned exchange-correlation functionals for density functional theory. All common functional types (LDA, GGA, metaGGA) as well as NeuralXC type functionals are supported. Libnxc is written in C++ and has Fortran bindings. An implementation in Python, pylibnxc is also available. Libnxc is inspired by Libxc, mirroring as closely as possible its API. In doing so, the integration of Libnxc in electronic structure codes that use Libxc should be straightforward. Libnxc can utilize multi-processing through MPI and model inference on GPUs through CUDA is supported as well. Although the primary motivation for Libnxc was to add support for neural network based functionals, other types of models can be used as well. As long as the following requirements are fulfilled, models can be used by Libnxc:
The model has to be implemented in PyTorch and serialized into a TorchScript model (e.g. with
torch.jit.trace)The model input and output has to follow the form specified in Functionals
The serialized model is regarded as a containerized black box by Libnxc. Thus, even simple polynomial models can be implemented and evaluated. While not replacing hard-coded functionals such as the ones employed by Libxc and directly by DFT codes, this approach provides several advantages:
Fast experimentation: Functionals can be quickly implemented and used in a plug-and-play manner
Automatic differentiation: PyTorch takes care of calculating all derivative terms needed in the exchange-correlation potential.
Native GPU support: PyTorch is designed to be run on GPUs using CUDA. This extends to serialized TorchScript models, thus evaluating libnxc functionals on GPUs is straightforward.