Skala: Accurate and scalable exchange-correlation with deep learning

Skala is a neural network-based exchange-correlation functional for density functional theory (DFT), developed by Microsoft Research AI for Science. It leverages deep learning to predict exchange-correlation energies from electron density features, achieving chemical accuracy for atomization energies and strong performance on broad thermochemistry and kinetics benchmarks, all at a computational cost similar to semi-local DFT.

Trained on a large, diverse dataset—including coupled cluster atomization energies and public benchmarks—Skala uses scalable message passing and local layers to learn both local and non-local effects. The model has about 276,000 parameters and matches the accuracy of leading hybrid functionals.

Learn more about Skala in our ArXiv paper.

What's in here

This repository contains three main components:

1. The Python package microsoft-skala, which is also distributed on PyPI and contains a Pytorch implementation of the Skala model, its hookups to quantum chemistry packages PySCF and ASE, and an independent client library for the Skala model served in Azure AI Foundry.
2. A development version of the CPU/GPU C++ library for XC functionals GauXC with an add-on supporting Pytorch-based functionals like Skala. GauXC is part of the stack that serves Skala in Azure AI Foundry and can be used to integrate Skala into other third-party DFT codes.
3. An example of using Skala in C++ CPU applications through LibTorch, see examples/cpp/cpp_integration.

All information below relates to the Python package, the development version of GauXC including its license and other information can be found in third_party/gauxc.

Getting started

Install using Pip:

pip install torch --index-url https://download.pytorch.org/whl/cpu  # unless you already have GPU Pytorch for something else
pip install microsoft-skala

Run an SCF calculation with Skala for a hydrogen molecule:

from pyscf import gto
from skala.pyscf import SkalaKS

mol = gto.M(
    atom="""H 0 0 0; H 0 0 1.4""",
    basis="def2-tzvp",
)
ks = SkalaKS(mol, xc="skala")
ks.kernel()

Go to microsoft.github.io/skala for a more detailed installation guide and further examples of how to use Skala functional with PySCF and ASE and in Azure Foundry.

Getting started (GPU support)

Install using Pip:

cu_version=128 #or 126 or 130 depending on your CUDA version
pip install torch cupy --extra-index-url "https://download.pytorch.org/whl/cu${cu_version}"
pip install --no-deps "gpu4pyscf-cuda${cu_version:0:2}x>=1.0,<2" "gpu4pyscf-libxc-cuda${cu_version:0:2}x>=0.4,<1"
pip install microsoft-skala

Run an SCF calculation with Skala for a hydrogen molecule on GPU:

from pyscf import gto
from skala.gpu4pyscf import SkalaKS

mol = gto.M(
    atom="""H 0 0 0; H 0 0 1.4""",
    basis="def2-tzvp",
)
ks = SkalaKS(mol, xc="skala")
ks.kernel()

Project information

See the following files for more information about contributing, reporting issues, and the code of conduct:

• CONTRIBUTING.md
• LICENSE.txt
• SECURITY.md

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.