Thank you for using molSimplify.

If you used molSimplifyLite for structure generation, please cite the following manuscript: Download RIS

@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},
}

If you used molSimplifyLite to predict spin splitting energetics, please cite the following manuscript: Download RIS

@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},
}

We have many other models and uses for molSimplify. Please visit the About page to get more information on the full version of molSimplify, as well as models for job success prediction, prediction of frontier orbital energetics, prediction of spin state dependent reaction energetics, and more.