Citation
========

molSimplify is research software. If you use it for work that results in a publication, please cite the following references:

Structure Generation
--------------------

::

	@Article {molSimplify,
	author = {Ioannidis, Efthymios I. and Gani, Terry Z. H. and Kulik, Heather J.},
	title = {molSimplify: A Toolkit for Automating Discovery in Inorganic Chemistry},
	journal = {Journal of Computational Chemistry},
	volume = {37},
	number = {22},
	pages = {2106--2117},
	issn = {1096-987X},
	url = {http://dx.doi.org/10.1002/jcc.24437},
	doi = {10.1002/jcc.24437},
	year = {2016},
	}

::

	@Article{Nandy2018IECR,
	author = {Nandy, Aditya and Duan, Chenru and Janet, Jon Paul and Gugler, Stefan and Kulik, Heather J.},
	title = {Strategies and Software for Machine Learning Accelerated Discovery in Transition Metal Chemistry},
	journal = {Industrial {\&} Engineering Chemistry Research},
	volume = {57},
	number = {42},
	pages = {13973-13986},
	issn = {0888-5885},
	url = {https://doi.org/10.1021/acs.iecr.8b04015},
	doi = {10.1021/acs.iecr.8b04015},
	year = {2018},
	}

Models and Representations
--------------------------

If you use any machine learning (ML) models in molSimplify that results in a publication, please cite the corresponding references.

If you use the machine learning (ML) models in molSimplify to predict metal-ligand bond lengths, please cite:

::

	@Article{Janet2017CS,
	author = {Janet, Jon Paul and Kulik, Heather J.},
	title = {Predicting Electronic Structure Properties of Transition Metal Complexes with Neural Networks",
	journal = {Chem. Sci.},
	year = {2017},
	volume = {8},
	issue = {7},
	pages = {5137-5152},
	url = {http://dx.doi.org/10.1039/C7SC01247K},
	doi = {10.1039/C7SC01247K},
	year = {2017},
	}

If you use the machine learning (ML) models in molSimplify to predict spin splitting energies and/or redox potentials, or RAC descriptors, please cite:

::

	@Article{Janet2017JPCA,
	author = {Janet, Jon Paul and Kulik, Heather J.},
	title = {Resolving Transition Metal Chemical Space: Feature Selection for Machine Learning and Structure--Property Relationships},
	journal = {The Journal of Physical Chemistry A},
	volume = {121},
	number = {46},
	pages = {8939-8954},
	issn = {1089-5639},
	url = {https://doi.org/10.1021/acs.jpca.7b08750},
	doi = {10.1021/acs.jpca.7b08750},
	year = {2017},
	}

::

	@Article{Janet2019IC,
	author = {Janet, Jon Paul and Liu, Fang and Nandy, Aditya and Duan, Chenru and Yang, Tzuhsiung and Lin, Sean and Kulik, Heather J.},
	title = {Designing in the Face of Uncertainty: Exploiting Electronic Structure and Machine Learning Models for Discovery in Inorganic Chemistry},
	journal = {Inorganic Chemistry},
	volume = {58},
	number = {16},
	pages = {10592-10606},
	issn = {0020-1669},
	url = {https://doi.org/10.1021/acs.inorgchem.9b00109},
	doi = {10.1021/acs.inorgchem.9b00109},
	year = {2019},
	}

If you use the machine learning (ML) models in molSimplify to predict the outcomes of your calculations, please cite:

::

	@Article{Duan2019JCTC,
	author = {Duan, Chenru and Janet, Jon Paul and Liu, Fang and Nandy, Aditya and Kulik, Heather J.},
	title = {Learning from Failure: Predicting Electronic Structure Calculation Outcomes with Machine Learning Models},
	journal = {Journal of Chemical Theory and Computation},
	volume = {15},
	number = {4},
	pages = {2331-2345},
	issn = {1549-9618},
	url={https://doi.org/10.1021/acs.jctc.9b00057},
	doi={10.1021/acs.jctc.9b00057},
	year={2019},
	}

If you use the machine learning (ML) models in molSimplify to predict the spin-state dependent reaction energetics, please cite:

::

	@Article{Nandy2019ACSCatal,
	author = {Nandy, Aditya and Zhu, Jiazhou and Janet, Jon Paul and Duan, Chenru and Getman, Rachel B. and Kulik, Heather J.},
	title = {Machine Learning Accelerates the Discovery of Design Rules and Exceptions in Stable Metal--Oxo Intermediate Formation},
	journal = {ACS Catalysis},
	volume = {9},
	number = {9},
	pages = {8243-8255},
	url = {https://doi.org/10.1021/acscatal.9b02165},
	doi = {10.1021/acscatal.9b02165},
	year={2019},
	}