Skip to main content

Skala Exchange Correlation Functional

Project description

Skala: Accurate and scalable exchange-correlation with deep learning

Documentation Tests PyPI Paper

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 two main components:

  1. The Python package 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.

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 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.

Project information

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

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.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

skala-1.0.0.tar.gz (3.8 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

skala-1.0.0-py3-none-any.whl (3.9 kB view details)

Uploaded Python 3

File details

Details for the file skala-1.0.0.tar.gz.

File metadata

  • Download URL: skala-1.0.0.tar.gz
  • Upload date:
  • Size: 3.8 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.5

File hashes

Hashes for skala-1.0.0.tar.gz
Algorithm Hash digest
SHA256 9834a8e39cbab662ccc6efa5d81c71f10486caa1c04f0f70486e17507c5f557a
MD5 a7bd8ad0d3302049e5ff165ba7d66548
BLAKE2b-256 cf4e7e1a927fccc6627a34b857deab46091106089cf32cc2a30b1192477f13d3

See more details on using hashes here.

File details

Details for the file skala-1.0.0-py3-none-any.whl.

File metadata

  • Download URL: skala-1.0.0-py3-none-any.whl
  • Upload date:
  • Size: 3.9 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/6.2.0 CPython/3.13.5

File hashes

Hashes for skala-1.0.0-py3-none-any.whl
Algorithm Hash digest
SHA256 95fdc736e4c66ef85c87d8122fe7fd94453b239e80449b9b7ae8e4c9ac7f89f0
MD5 83392bbd91309597f49ad30947e670c7
BLAKE2b-256 61fc9c756db28243f7f8e152a9f31a71f15110d87211e68e3417907b47e8f2a5

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page