This is an initial library of BibTex references for the torchani_step plug-in for SEAMM,
generated automatically by the cookiecutter. You may wish to edit the first entry to correct some
of the information.

Add other references to the end of the file and add code in the plug-in to cite the references as
appropriate. This is done with code like this:

        self.references.cite(
            raw=self._bibliography['doi:10.1002/jcc.21224'],
            alias='packmol',
            module='packmol_step',
            level=1,
            note='The principle PACKMOL citation.'
        )

which is typically in the run() method of the plug-in.

The base node class in SEAMM automatically reads this list of references into a dictionary in
self._bibliography with the key being the key of the BibTex entry -- usually the name on the
first line of the entry after the 'article' or 'misc' (for software) keyword.

See the documentation for the Reference Handler for the other arguments to cite().

# End of initial comments #

# This is the citation for this plug-in, generated and used automatically.
# You might need to edit the author, address, etc. The month, year and version fields
# are automatically filled in with information about current version of the plug-in.
# This is handled in seamm/nody.py, the run() method.

@Misc{torchani_step,
	author = {Paul Saxe},
	title = {Torchani plug-in for SEAMM},
	month = {$month},
	year = {$year},
	organization = {The Molecular Sciences Software Institute},
	url = {https://github.com/molssi-seamm/torchani_step },
	address = {USA},
	version = {$version}
}

@article{TorchANI,
    author = {Gao, Xiang and Ramezanghorbani, Farhad and Isayev, Olexandr and Smith,
                  Justin S. and Roitberg, Adrian E.},
    title = {TorchANI: A Free and Open Source PyTorch-Based Deep Learning Implementation
                  of the ANI Neural Network Potentials},
    journal = {Journal of Chemical Information and Modeling},
    volume = {60},
    number = {7},
    pages = {3408-3415},
    year = {2020},
    doi = {10.1021/acs.jcim.0c00451},
    note ={PMID: 32568524},
    URL = {https://doi.org/10.1021/acs.jcim.0c00451},
    eprint = {https://doi.org/10.1021/acs.jcim.0c00451}
}

@Article{ANI,
    author ="Smith, J. S. and Isayev, O. and Roitberg, A. E.",
    title ="ANI-1: an extensible neural network potential with DFT accuracy at force
                  field computational cost",
    journal ="Chem. Sci.",
    year ="2017",
    volume ="8",
    issue ="4",
    pages ="3192-3203",
    publisher ="The Royal Society of Chemistry",
    doi ="10.1039/C6SC05720A",
    url ="http://dx.doi.org/10.1039/C6SC05720A"
}

@article{ANI_dataset, 
    year = {2017}, 
    title = {{ANI-1, A data set of 20 million calculated off-equilibrium conformations for organic molecules}}, 
    author = {Smith, Justin S. and Isayev, Olexandr and Roitberg, Adrian E.}, 
    journal = {Scientific Data}, 
    doi = {10.1038/sdata.2017.193}, 
    pmid = {29257127}, 
    pmcid = {PMC5735918}, 
    eprint = {1708.04987}, 
    pages = {170193}, 
    number = {1}, 
    volume = {4}, 
    url  ="http://dx.doi.org/10.1039/C6SC05720A"
}

@article{ANI-1x,
    author = {Smith,Justin S.  and Nebgen,Ben  and Lubbers,Nicholas  and Isayev,Olexandr  and Roitberg,Adrian E. },
    title = {Less is more: Sampling chemical space with active learning},
    journal = {The Journal of Chemical Physics},
    volume = {148},
    number = {24},
    pages = {241733},
    year = {2018},
    doi = {10.1063/1.5023802},
    URL = {https://doi.org/10.1063/1.5023802},
    eprint = {https://doi.org/10.1063/1.5023802}
}

@article{ANI-1ccx, 
    year = {2019}, 
    title = {{Approaching coupled cluster accuracy with a general-purpose neural network
                  potential through transfer learning}}, 
    author = {Smith, Justin S. and Nebgen, Benjamin T. and Zubatyuk, Roman and Lubbers,
                  Nicholas and Devereux, Christian and Barros, Kipton and Tretiak,
                  Sergei and Isayev, Olexandr and Roitberg, Adrian E.}, 
    journal = {Nature Communications}, 
    doi = {10.1038/s41467-019-10827-4}, 
    pmid = {31263102}, 
    pmcid = {PMC6602931}, 
    pages = {2903}, 
    number = {1}, 
    volume = {10}, 
    URL = {https://doi.org/10.1038/s41467-019-10827-4}
}

@article{ANI-2x,
    author = {Devereux, Christian and Smith, Justin S. and Huddleston, Kate K. and
                  Barros, Kipton and Zubatyuk, Roman and Isayev, Olexandr and Roitberg,
                  Adrian E.},
    title = {Extending the Applicability of the ANI Deep Learning Molecular Potential to
                  Sulfur and Halogens},
    journal = {Journal of Chemical Theory and Computation},
    volume = {16},
    number = {7},
    pages = {4192-4202},
    year = {2020},
    doi = {10.1021/acs.jctc.0c00121},
    note ={PMID: 32543858},
    URL = {https://doi.org/10.1021/acs.jctc.0c00121},
    eprint = {https://doi.org/10.1021/acs.jctc.0c00121}
}

@article{ase, 
    year = {2017}, 
    title = {{The atomic simulation environment—a Python library for working with
                  atoms}}, 
    author = {Larsen, Ask Hjorth and Mortensen, Jens Jørgen and Blomqvist, Jakob and
                  Castelli, Ivano E and Christensen, Rune and Dułak, Marcin and Friis,
                  Jesper and Groves, Michael N and Hammer, Bjørk and Hargus, Cory and
                  Hermes, Eric D and Jennings, Paul C and Jensen, Peter Bjerre and
                  Kermode, James and Kitchin, John R and Kolsbjerg, Esben Leonhard and
                  Kubal, Joseph and Kaasbjerg, Kristen and Lysgaard, Steen and
                  Maronsson, Jón Bergmann and Maxson, Tristan and Olsen, Thomas and
                  Pastewka, Lars and Peterson, Andrew and Rostgaard, Carsten and
                  Schiøtz, Jakob and Schütt, Ole and Strange, Mikkel and Thygesen,
                  Kristian S and Vegge, Tejs and Vilhelmsen, Lasse and Walter, Michael
                  and Zeng, Zhenhua and Jacobsen, Karsten W}, 
    journal = {Journal of Physics: Condensed Matter}, 
    issn = {0953-8984}, 
    doi = {10.1088/1361-648x/aa680e}, 
    pmid = {28323250}, 
    pages = {273002}, 
    number = {27}, 
    volume = {29}, 
    URL = {https://iopscience.iop.org/article/10.1088/1361-648X/aa680e/meta}
}

