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.
This repository contains two main components:
- 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. - 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.
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
.
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.
See the following files for more information about contributing, reporting issues, and the code of conduct:
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