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.

• 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.

Once a stable version is released, users can directly install the pakcage from PyPI using the pip call.

# 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-4-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 tutorials. An inbuilt 15 picosecond MD-trajectory of benzene is also provided as an inbuilt dataset for preliminary exploration of the package.

You can view the documentation at https://vivinvinod.github.io/mfml-4-qc/. 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 the code repository.