License and citations¶
License¶
MLatom is open-source software under the Apache License 2.0.
Copyright 2013-2026 Pavlo O. Dral (dr-dral.com)
Licensed under the Apache License, Version 2.0 (the “License”); you may not use this software except in compliance with the License. You may obtain a copy of the License at apache.org/licenses/LICENSE-2.0.
When using MLatom or its derivatives in scientific publications, please cite it as:
Pavlo O. Dral, Fuchun Ge, Yi-Fan Hou, Peikun Zheng, Yuxinxin Chen, Mario Barbatti, Olexandr Isayev, Cheng Wang, Bao-Xin Xue, Max Pinheiro Jr, Yuming Su, Yiheng Dai, Yangtao Chen, Lina Zhang, Shuang Zhang, Arif Ullah, Quanhao Zhang, Yanchi Ou. MLatom 3: A Platform for Machine Learning-enhanced Computational Chemistry Simulations and Workflows, J. Chem. Theory Comput. 2024, 20, 1193–1213. DOI: 10.1021/acs.jctc.3c01203.
if you use MLatom for the NAMD simulations, please also cite:
FSSH: Jakub Martinka, Mikołaj Martyka, Biman Medhi, Jiří Pittner, and Pavlo O. Dral. Flexible Framework for Surface Hopping: From Hybrid Schemes for Machine Learning to Benchmarkable Nonadiabatic Dynamics. J. Chem. Theory Comput. 2026, 22, 2467–2479. DOI: 10.1021/acs.jctc.5c02137. Preprint on arXiv: https://arxiv.org/abs/2512.19152.
LZBL: Lina Zhang, Sebastian Pios, Mikołaj Martyka, Fuchun Ge, Yi-Fan Hou, Yuxinxin Chen, Joanna Jankowska, Lipeng Chen, Mario Barbatti, Pavlo O. Dral. MLatom software ecosystem for surface hopping dynamics in Python with quantum mechanical and machine learning methods. J. Chem. Theory Comput. 2024, 20, 5043–5057. DOI: 10.1021/acs.jctc.4c00468. Preprint on arXiv: https://arxiv.org/abs/2404.06189.
Pavlo O. Dral, Fuchun Ge, Yi-Fan Hou, Yuxinxin Chen, Mikołaj Martyka, Jakub Martinka, Peikun Zheng, Lina Zhang, Xin-Yu Tong, Bao-Xin Xue, Quanhao Zhang, Arif Ullah, Sebastian V. Pios, Vignesh B. Kumar, Yanchi Ou, Max Pinheiro Jr, Yuming Su, Yiheng Dai, Yangtao Chen, Shuang Zhang, Jinming Hu, Matheus O. Bispo, MLatom: A Package for Atomistic Simulations with Machine Learning, version [add version number], Xiamen University, Xiamen, China, 2013-2026. MLatom.com.
The citations for MLatom’s interfaces and features should also be included. When you run MLatom from the command line or an input file, it prints the citations for the features you used at the top of the output; the underlying lists are header.py and ref.json.
Warning
The printed list does not cover everything. It is produced from the command-line arguments, so features that are only available through the Python API – surface-hopping dynamics, MDtrajNet, MS-ANI, conical-intersection optimization, and others – print nothing. For those, take the citation from the tutorial of the feature you used:
feature |
where the citation is |
|---|---|
the corresponding method tutorial |
|
the corresponding model tutorial |
|
the corresponding tutorial |
|
surface hopping, MS-ANI and active learning for TSH, GapMD, conical intersections |
the corresponding excited-state tutorial |
IR and Raman spectra, vibrational spectra from MD, UV/vis |
the corresponding spectra tutorial |
the corresponding simulation tutorial |
See also MLatom.com.
Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an “AS IS” BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License.
Citations¶
Citations mentioned above should be included. For convenience, below we provide some of the citations in the Bibtex format and you can also download EndNote files.
@article{MLatom 3,
author = {Dral, Pavlo O. and Ge, Fuchun and Hou, Yi-Fan and Zheng, Peikun and Chen, Yuxinxin and Barbatti, Mario and Isayev, Olexandr and Wang, Cheng and Xue, Bao-Xin and Pinheiro Jr, Max and Su, Yuming and Dai, Yiheng and Chen, Yangtao and Zhang, Shuang and Zhang, Lina and Ullah, Arif and Zhang, Quanhao and Ou, Yanchi},
title = {MLatom 3: A Platform for Machine Learning-Enhanced Computational Chemistry Simulations and Workflows},
journal = {J. Chem. Theory Comput.},
volume = {20},
number = {3},
pages = {1193--1213},
DOI = {10.1021/acs.jctc.3c01203},
year = {2024},
type = {Journal Article}
}
@misc{MLatomProg,
author = {Dral, Pavlo O. and Ge, Fuchun and Hou, Yi-Fan and Chen, Yuxinxin and Martyka, Mikołaj and Martinka, Jakub and Zheng, Peikun and Zhang, Lina and Tong, Xin-Yu and Xue, Bao-Xin and Zhang, Quanhao and Ullah, Arif and Pios, Sebastian V. and Kumar, Vignesh B. and Ou, Yanchi and Pinheiro Jr, Max and Su, Yuming and Dai, Yiheng and Chen, Yangtao and Zhang, Shuang and Hu, Jinming and Bispo, Matheus O.},
title = {MLatom: A Package for Atomistic Simulations with Machine Learning},
year = {2013--2026},
type = {Computer Program}
}
@article{FSSH,
author = {Martinka, Jakub and Martyka, Mikołaj and Medhi, Biman and Pittner, Jiří and Dral, Pavlo O.},
title = {Flexible Framework for Surface Hopping: From Hybrid Schemes for Machine Learning to Benchmarkable Nonadiabatic Dynamics},
journal = {J. Chem. Theory Comput.},
volume = {22},
number = {5},
pages = {2467--2479},
DOI = {10.1021/acs.jctc.5c02137},
year = {2026},
type = {Journal Article}
}
@article{LSBL,
author = {Zhang, L. and Pios, S. V. and Martyka, M. and Ge, F. and Hou, Y. F. and Chen, Y. and Chen, L. and Jankowska, J. and Barbatti, M. and Dral, P. O.},
title = {MLatom Software Ecosystem for Surface Hopping Dynamics in Python with Quantum Mechanical and Machine Learning Methods},
journal = {J. Chem. Theory Comput.},
volume = {20},
number = {12},
pages = {5043--5057},
DOI = {10.1021/acs.jctc.4c00468},
year = {2024},
type = {Journal Article}
}
@article{MLatom2,
author = {Dral, Pavlo O. and Ge, Fuchun and Xue, Bao-Xin and Hou, Yi-Fan and Pinheiro Jr, Max and Huang, Jianxing and Barbatti, Mario},
title = {MLatom 2: An Integrative Platform for Atomistic Machine Learning},
journal = {Top. Curr. Chem.},
volume = {379},
number = {4},
pages = {27},
DOI = {10.1007/s41061-021-00339-5},
year = {2021},
type = {Journal Article}
}
@article{MLatom1,
author = {Dral, Pavlo O.},
title = {MLatom: A Program Package for Quantum Chemical Research Assisted by Machine Learning},
journal = {J. Comput. Chem.},
volume = {40},
number = {26},
pages = {2339--2347},
DOI = {10.1002/jcc.26004},
year = {2019},
type = {Journal Article}
}