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

You need a fortran compiler, OpenMP and a math library. Default is gfortran and openblas.

sudo apt install gcc libomp-dev libopenblas-dev

If you are on mac, you can install gcc, OpenML and BLAS/Lapack via brew

brew install gcc libomp openblas lapack

You can then install via PyPi

pip install qmllib

or directly from github

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

or if you want a specific feature branch

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

How to contribute

Know a issue and want to get started developing? Fork it, clone it, make it , test it.

git clone your_repo qmllib.git
cd qmllib.git
make # setup env
make compile # compile

You know have a conda environment in ./env and are ready to run

make test

happy developing

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

  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

• Columb Matrix generate_columnb_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

What is left to do?

• Compile based on FCC env variable
• if ifort find the right flags
• Find MKL from env (for example conda)
• Find what numpy has been linked too (lapack or mkl)
• Notebook examples