diff --git a/.github/workflows/test.yml b/.github/workflows/test.yml index 5774cf8e..19006dc5 100644 --- a/.github/workflows/test.yml +++ b/.github/workflows/test.yml @@ -28,3 +28,5 @@ jobs: - run: pip install . -v - run: make test python=python - run: make format python=python + - run: make build python=python + - run: make test-dist python=python diff --git a/Makefile b/Makefile index 0e63be59..d9b7c650 100644 --- a/Makefile +++ b/Makefile @@ -29,6 +29,9 @@ format: test: ${python} -m pytest -rs ./tests +test-dist: + ${python} -m twine check dist/* + types: ${python} -m monkeytype run $$(which ${pytest}) ./tests ${python} -m monkeytype list-modules | grep ${pkg} | parallel -j${j} "${python} -m monkeytype apply {} > /dev/null && echo {}" diff --git a/README.rst b/README.rst index e128b247..07cac449 100644 --- a/README.rst +++ b/README.rst @@ -88,47 +88,55 @@ How to cite Please cite the representation that you are using accordingly. -- | **Implementation** +- **Implementation** + Toolkit for Quantum Chemistry Machine Learning, https://github.com/qmlcode/qmllib, -- | **FCHL19** ``generate_fchl19`` +- **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`` +- **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_*`` +- **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`` +- **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`` +- **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`` +- **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`` +- **AARAD** ``generate_aarad`` + Alchemical and structural distribution based representation for universal quantum machine learning, Faber, Christensen, Huang, Lilienfeld, J. Chem. Phys. 148, 241717 (2018),