qmllib 1.2.0

What is qmllib?

qmllib is a Python/Fortran toolkit for representation of molecules and solids for machine learning of properties of molecules and solids. The library is not a high-level framework where you can do model.train(), but supplies the building blocks to carry out efficient and accurate machine learning. As such, the goal is to provide usable and efficient implementations of concepts such as representations and kernels.

QML or qmllib?

qmllib represents the core library functionality derived from the original QML package, providing a powerful toolkit for quantum machine learning applications, but without the high-level abstraction, for example SKLearn.

This package is and should stay free-function design oriented.

If you are moving from qml to qmllib, note that there are breaking changes to the interface to make it more consistent with both argument orders and function naming.

How to install

Install from PyPI — pre-built wheels are available for Linux and macOS. They are pre-compiled with optimized BLAS libraries and OpenMP support.

For most users, you can just install with pip:

pip install qmllib

This installs pre-compiled wheels with optimized BLAS libraries:

• Linux: OpenBLAS
• macOS: Apple Accelerate framework

Installing from source

If you are installing from source (e.g. directly from GitHub), you will need a Fortran compiler, OpenMP and a BLAS library. On Linux:

sudo apt install gfortran libomp-dev libopenblas-dev

On macOS via Homebrew:

brew install gcc libomp llvm 

Or install directly from GitHub:

pip install git+https://github.com/qmlcode/qmllib

Or a specific branch:

pip install git+https://github.com/qmlcode/qmllib@feature_branch

How to contribute

uv is required for the development workflow.

Fork and clone the repo, then set up the environment and run the tests:

git clone your_repo qmllib.git
cd qmllib.git
make install-dev
make test

Fork it, clone it, make it, test it!

How to use

Notebook examples are coming. For now, see test files in tests/*.

How to cite

Please cite the representation that you are using accordingly.

• Implementation

  qmllib: A Python Toolkit for Quantum Chemistry Machine Learning, https://github.com/qmlcode/qmllib, <version or git commit>

• FCHL19 generate_fchl19

  FCHL revisited: Faster and more accurate quantum machine learning, Christensen, Bratholm, Faber, Lilienfeld, J. Chem. Phys. 152, 044107 (2020), https://doi.org/10.1063/1.5126701

• FCHL18 generate_fchl18

  Alchemical and structural distribution based representation for universal quantum machine learning, Faber, Christensen, Huang, Lilienfeld, J. Chem. Phys. 148, 241717 (2018), https://doi.org/10.1063/1.5020710

• Coulomb Matrix generate_coulomb_matrix_*

  Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning, Rupp, Tkatchenko, Müller, Lilienfeld, Phys. Rev. Lett. 108, 058301 (2012) DOI: https://doi.org/10.1103/PhysRevLett.108.058301

• Bag of Bonds (BoB) generate_bob

  Assessment and Validation of Machine Learning Methods for Predicting Molecular Atomization Energies, Hansen, Montavon, Biegler, Fazli, Rupp, Scheffler, Lilienfeld, Tkatchenko, Müller, J. Chem. Theory Comput. 2013, 9, 8, 3404–3419 https://doi.org/10.1021/ct400195d

• SLATM generate_slatm

  Understanding molecular representations in machine learning: The role of uniqueness and target similarity, Huang, Lilienfeld, J. Chem. Phys. 145, 161102 (2016) https://doi.org/10.1063/1.4964627

• ACSF generate_acsf

  Atom-centered symmetry functions for constructing high-dimensional neural network potentials, Behler, J Chem Phys 21;134(7):074106 (2011) https://doi.org/10.1063/1.3553717

• AARAD generate_aarad

  Alchemical and structural distribution based representation for universal quantum machine learning, Faber, Christensen, Huang, Lilienfeld, J. Chem. Phys. 148, 241717 (2018), https://doi.org/10.1063/1.5020710