Multifidelity Machine Learning for Quantum Chemistry (MFML-4-QC)

MFML-4-QC is an open-source library that enables multifidelity machine learning for quantum chemical systems. While the multifidelity methods are model-architecture agnostic, this library provides a lightweight, ultra-fast Numba-compiled Kernel Ridge Regression (KRR) setup as the primary architecture. Users can seamlessly integrate their own custom ML models (e.g., from scikit-learn) and directly interface with quantum chemistry engines like ORCA and PySCF for automated data generation and active learning.

If you find a bug in MFML-4-QC, or have a feature request, please open a GitHub issue.

• Ultra-Fast Kernels: Compute Matérn, Gaussian, Laplacian, and Wasserstein kernels efficiently using JIT-compiled C-loops via Numba.
• In-Memory Representations: Generate flattened Coulomb Matrices directly from .xyz trajectories without slow disk I/O.
• Flexible ML Architectures: Use the built-in KRR or drop in any scikit-learn compatible estimator (e.g., RandomForestRegressor, MLPRegressor).
• Quantum Chemistry Oracles: Abstract interfaces to automatically generate inputs, execute runs, and parse outputs from engines like ORCA and PySCF.

Prerequisites: Python 3.10 or higher. It is best to install within a fresh conda environment to avoid dependency clashes.

Users can directly install the pakcage from PyPI using the pip call. The package is available at https://pypi.org/project/mfml-4-qc/1.0.0/ and can be installed with the following steps:

# Create and activate a fresh conda environment
conda create -n mfmlenv python=3.10 -y
conda activate mfmlenv

# Install the package
pip install mfml-4-qc

You can also directly download the GitHub repo and install the package from there.

# Clone the repository
git clone https://github.com/vivinvinod/mfml-qc.git

# Create and activate a fresh conda environment
conda create -n mfmlenv python=3.10 -y
conda activate mfmlenv

# Install the package
pip install .

If you plan to use the built-in PySCFEngine oracle, you can install the package with the optional PySCF dependency. (Note: PySCF can be a heavy dependency, which is why it is kept optional).

pip install .[pyscf]

TO run the ORCAEngine you will need to install ORCA. See the official ORCA manual for details on how to do so.

If you are beta testing, modifying the source code, or want to build the local documentation, install the package in "editable" mode (-e) with the [dev] flag. This installs testing tools (pytest, black etc) and the Sphinx documentation stack:

pip install -e .[dev]

MFML-4-QC comes with a comprehensive documentation site that includes detailed API references and copy-pasteable tutorials.

To view the documentation locally, go to docs/build/html/ and open index.html in a browser of your choice.

You can also browse the raw tutorial scripts directly in the examples/ directory of this repository.

If you use this package, please consider citing the following articles:

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