@article{huang2020quantum,
  title={Quantum machine learning using atom-in-molecule-based fragments selected on the fly},
  author={Huang, Bing and von Lilienfeld, O A},
  journal={Nat. Chem.},
  volume={12},
  number={10},
  pages={945--951},
  year={2020},
  publisher={Nature Publishing Group}
}

@article{bartok_2013_soap,
    author = {Bart{\'{o}}k, Albert P. and Kondor, Risi and Cs{\'{a}}nyi, G{\'{a}}bor},
    journal = {Phys. Rev. B},
    pages = {184115},
    title = {{On representing chemical environments}},
    volume = {87},
    year = {2013}
}

@article{faber_alchemical_2018,
	title = {Alchemical and structural distribution based representation for universal quantum machine learning},
	volume = {148},
	number = {24},
	journal = {J. Chem. Phys.},
	author = {Faber, Felix A. and Christensen, Anders S. and Huang, Bing and von Lilienfeld,  O.  Anatole},
	month = jun,
	year = {2018},
	pages = {241717}
}

@article{christensen2020fchl,
  title={FCHL revisited: Faster and more accurate quantum machine learning},
  author={Christensen, Anders S and Bratholm, Lars A and Faber, Felix A and von Lilienfeld, O A},
  journal={J. Chem. Phys.},
  volume={152},
  number={4},
  pages={044107},
  year={2020},
  publisher={AIP Publishing LLC}
}

@article{rupp2012fast,
 author = {Rupp, Matthias and Tkatchenko, Alexandre and M\"{u}ller, Klaus-Robert and von Lilienfeld, O A},
 title = {Fast and Accurate Modeling of Molecular Atomization Energies with Machine Learning},
 journal = {Phys. Rev. Lett.},
 volume = {108},
 number = {5},
 pages = {058301},
 year = {2012},
}

@article{huang2016communication,
 author = {Huang, Bing and von Lilienfeld, O A},
 title = {Communication: {Understanding} molecular representations in machine learning: {The} role of uniqueness and target similarity},
 journal = {J. Chem. Phys.},
 year = {2016},
 volume = {145},
 pages = {161102},
 publisher = {AIP Publishing},
}

@article{musil2021physics,
  title={Physics-inspired structural representations for molecules and materials},
  author={Musil, Felix and Grisafi, Andrea and Bart{\'o}k, Albert P and Ortner, Christoph and Cs{\'a}nyi, G{\'a}bor and Ceriotti, Michele},
  journal={Chem. Rev.},
  volume={121},
  number={16},
  pages={9759--9815},
  year={2021},
  publisher={ACS Publications}
}

@article{von2018quantum,
  title={Quantum machine learning in chemical compound space},
  author={Von Lilienfeld, O Anatole},
  journal={Angew. Chem. Int. Ed.},
  volume={57},
  number={16},
  pages={4164--4169},
  year={2018},
  publisher={Wiley Online Library}
}

@article{li2015molecular,
  title={Molecular dynamics with on-the-fly machine learning of quantum-mechanical forces},
  author={Li, Zhenwei and Kermode, James R and De Vita, Alessandro},
  journal={Phys. Rev. Lett.},
  volume={114},
  number={9},
  pages={096405},
  year={2015},
  publisher={APS}
}

@article{chmiela_machine_2017,
	title = {Machine learning of accurate energy-conserving molecular force fields},
	volume = {3},
	number = {5},
	journal = {Sci. Adv.},
	author = {Chmiela, Stefan and Tkatchenko, Alexandre and Sauceda, Huziel E. and Poltavsky, Igor and Sch\"{u}tt, Kristof T. and M\"{u}ller, Klaus-Robert},
	year = {2017},
	pages = {e1603015},
	file = {1603015_SM.pdf:/home/benjamin/Zotero/storage/GXIDFGQG/1603015_SM.pdf:application/pdf;e1603015.full.pdf:/home/benjamin/Zotero/storage/K45WDNX4/e1603015.full.pdf:application/pdf}
}

@article{chmiela_towards_2018,
	title = {Towards exact molecular dynamics simulations with machine-learned force fields},
	volume = {9},
	number = {1},
	journal = {Nat. Commun.},
	author = {Chmiela, Stefan and Sauceda, Huziel E. and M\"{u}ller, Klaus-Robert and Tkatchenko, Alexandre},
	year = {2018},
}

@article{bereau_transferable_2015,
	title = {Transferable {Atomic} {Multipole} {Machine} {Learning} {Models} for {Small} {Organic} {Molecules}},
	volume = {11},
	number = {7},
	journal = {J. Chem. Theory Comput.},
	author = {Bereau, Tristan and Andrienko, Denis and von Lilienfeld, O A},
	month = jul,
	year = {2015},
	pages = {3225--3233},
}

@article{grisafi2018symmetry,
  title={Symmetry-adapted machine learning for tensorial properties of atomistic systems},
  author={Grisafi, Andrea and Wilkins, David M and Cs{\'a}nyi, G{\'a}bor and Ceriotti, Michele},
  journal={Phys. Rev. Lett.},
  volume={120},
  number={3},
  pages={036002},
  year={2018},
  publisher={APS}
}

@article{grisafi2018transferable,
  title={Transferable Machine-Learning Model of the Electron Density},
  author={Grisafi, Andrea and Fabrizio, Alberto and Meyer, Benjamin and Wilkins, David M and Corminboeuf, Clemence and Ceriotti, Michele},
  journal={ACS Cent. Sci.},
  volume={5},
  number={1},
  pages={57--64},
  year={2018},
  publisher={ACS Publications}
}

@article{wilkins_accurate_nodate,
  title={Accurate molecular polarizabilities with coupled cluster theory and machine learning},
  author={Wilkins, David M and Grisafi, Andrea and Yang, Yang and Lao, Ka Un and DiStasio, Robert A and Ceriotti, Michele},
  journal={Proc. Natl. Acad. Sci. U.S.A.},
  volume={116},
  number={9},
  pages={3401--3406},
  year={2019},
  publisher={National Acad Sciences}
}

@article{bartok2010gaussian,
 author = {Bart\'ok, Albert P. and Payne, Mike C. and Kondor, Risi and Cs\'anyi, G\'abor},
 title = {{Gaussian} Approximation Potentials: {The} Accuracy of Quantum Mechanics, without the Electrons},
 journal = {Phys. Rev. Lett.},
 volume = {104},
 number = {13},
 pages = {136403},
 year = {2010},
}

@article{fabrizio2019electron,
  title={Electron density learning of non-covalent systems},
  author={Fabrizio, Alberto and Grisafi, Andrea and Meyer, Benjamin and Ceriotti, Michele and Corminboeuf, Clemence},
  journal={Chem. Sci.},
  volume={10},
  number={41},
  pages={9424--9432},
  year={2019},
  publisher={Royal Society of Chemistry}
}

@article{westermayr_machine_2019,
	title = {Machine learning enables long time scale molecular photodynamics simulations},
	volume = {10},
	number = {35},
	journal = {Chem. Sci.},
	author = {Westermayr, Julia and Gastegger, Michael and Menger, Maximilian F. S. J. and Mai, Sebastian and Gonz\'alez, Leticia and Marquetand, Philipp},
	year = {2019},
	pages = {8100--8107}
}

@article{ceriotti_unsupervised_2019,
	title = {Unsupervised machine learning in atomistic simulations, between predictions and understanding},
	volume = {150},
	number = {15},
	journal = {J. Chem. Phys.},
	author = {Ceriotti, Michele},
	month = apr,
	year = {2019},
	pages = {150901},
}

@article{glielmo2021unsupervised,
  title={Unsupervised learning methods for molecular simulation data},
  author={Glielmo, Aldo and Husic, Brooke E and Rodriguez, Alex and Clementi, Cecilia and No{\'e}, Frank and Laio, Alessandro},
  journal={Chem. Rev.},
  volume={121},
  number={16},
  pages={9722--9758},
  year={2021},
  publisher={ACS Publications}
}

@article{cheng2020mapping,
  title={Mapping materials and molecules},
  author={Cheng, Bingqing and Griffiths, Ryan-Rhys and Wengert, Simon and Kunkel, Christian and Stenczel, Tamas and Zhu, Bonan and Deringer, Volker L and Bernstein, Noam and Margraf, Johannes T and Reuter, Karsten and others},
  journal={Accounts of Chemical Research},
  volume={53},
  number={9},
  pages={1981--1991},
  year={2020},
  publisher={ACS Publications}
}

@article{van2008visualizing,
  title={Visualizing data using t-SNE.},
  author={Van der Maaten, Laurens and Hinton, Geoffrey},
  journal={J. Mach. Learn. Res.},
  volume={9(11)},
  year={2008}
}

@article{ceriotti_2013_sketchmap,
    title="{Demonstrating the transferability and the descriptive power of sketch-map}",
    author="Ceriotti, M. and Tribello, G. A. and Parrinello, M.",
    journal="J. Chem. Theory Comput.",
    volume="9",
    pages="1521",
    year="2013"
}

@article{gallarati2021reaction,
  title={Reaction-based machine learning representations for predicting the enantioselectivity of organocatalysts},
  author={Gallarati, Simone and Fabregat, Raimon and Laplaza, Rub{\'e}n and Bhattacharjee, Sinjini and Wodrich, Matthew D and Corminboeuf, Clemence},
  journal={Chem. Sci.},
  volume={12},
  number={20},
  pages={6879--6889},
  year={2021},
  publisher={Royal Society of Chemistry}
}

@article{mahoney2009cur,
  title={CUR matrix decompositions for improved data analysis},
  author={Mahoney, Michael W and Drineas, Petros},
  journal={Proc. Natl. Acad. Sci. U.S.A.},
  volume={106},
  number={3},
  pages={697--702},
  year={2009},
  publisher={National Acad Sciences}
}

@book{kuhn2013applied,
  title={Applied Predictive Modelling},
  author={Kuhn, Max and Johnson, Kjell},
  volume={26},
  year={2013},
  note = {New York, USA},
  publisher={Springer}
}

@article{kulis2012metric,
  title={Metric learning: A survey},
  author={Kulis, Brian and others},
  journal={Found. Trends Mach. Learn.},
  volume={5},
  number={4},
  pages={287--364},
  year={2012}
}

@article{yang2006distance,
  title={Distance metric learning: A comprehensive survey},
  author={Yang, Liu and Jin, Rong},
  journal={Michigan State Universiy},
  volume={2(2)},
  pages={4},
  year={2006}
}

@article{jeon2019resimnet,
  title={ReSimNet: drug response similarity prediction using Siamese neural networks},
  author={Jeon, Minji and Park, Donghyeon and Lee, Jinhyuk and Jeon, Hwisang and Ko, Miyoung and Kim, Sunkyu and Choi, Yonghwa and Tan, Aik-Choon and Kang, Jaewoo},
  journal={Bioinformatics},
  volume={35},
  number={24},
  pages={5249--5256},
  year={2019},
  publisher={Oxford University Press}
}

@article{zheng2019sense,
  title={SENSE: Siamese neural network for sequence embedding and alignment-free comparison},
  author={Zheng, Wei and Yang, Le and Genco, Robert J and Wactawski-Wende, Jean and Buck, Michael and Sun, Yijun},
  journal={Bioinformatics},
  volume={35},
  number={11},
  pages={1820--1828},
  year={2019},
  publisher={Oxford University Press}
}

@article{nasser2020improved,
  title={Improved deep learning based method for molecular similarity searching using stack of deep belief networks},
  author={Nasser, Maged and Salim, Naomie and Hamza, Hentabli and Saeed, Faisal and Rabiu, Idris},
  journal={Molecules},
  volume={26},
  number={1},
  pages={128},
  year={2020},
  publisher={MDPI}
}

@article{coupry2022application,
  title={Application of deep metric learning to molecular graph similarity},
  author={Coupry, Damien E and Pog{\'a}ny, Peter},
  journal={J. Cheminf.},
  volume={14},
  number={1},
  pages={1--12},
  year={2022},
  publisher={BioMed Central}
}

@inproceedings{Kilian2007,
  title={Metric learning for kernel regression},
  author={Weinberger, Kilian Q and Tesauro, Gerald},
  booktitle={Artificial intelligence and statistics},
  pages={612--619},
  year={2007},
  note={PMLR}
}

@inproceedings{koch2015siamese,
  title={Siamese neural networks for one-shot image recognition},
  author={Koch, Gregory and Zemel, Richard and Salakhutdinov, Ruslan and others},
  booktitle={ICML deep learning workshop},
  note={Vol. 2},
  year={2015},
  organization={Lille}
}

@inproceedings{hoffer2015deep,
  title={Deep metric learning using triplet network},
  author={Hoffer, Elad and Ailon, Nir},
  booktitle={International workshop on similarity-based pattern recognition},
  pages={84--92},
  year={2015},
  organization={Springer}
}

@article{ramakrishnan_quantum_2014,
	title = {Quantum chemistry structures and properties of 134 kilo molecules},
	volume = {1},
	journal = {Sci. Data},
	author = {Ramakrishnan, Raghunathan and Dral, Pavlo O. and Rupp, Matthias and von Lilienfeld, O A},
	year = {2014},
}

@inproceedings{mahalanobis1936generalized,
  title={On the generalized distance in statistics},
  author={Mahalanobis, Prasanta Chandra},
  year={1936},
  organization={National Institute of Science of India}
}

@article{de2020metric,
  title={metric-learn: Metric Learning Algorithms in Python.},
  author={De Vazelhes, William and Carey, CJ and Tang, Yuan and Vauquier, Nathalie and Bellet, Aur{\'e}lien},
  journal={J. Mach. Learn. Res.},
  volume={21},
  pages={138--1},
  year={2020}
}

@misc{qml_code,
title={QML: A Python Toolkit for Quantum Machine Learning},
author={Anders S. Christensen and Felix A. Faber and Bing Huang and Lars A. Bratholm and Alexandre Tkatchenko and Klaus-Robert M\"{u}ller and O. Anatole von Lilienfeld},
year={2017},
url={https://github.com/qmlcode/qml}
}

@ARTICLE{jones2001scipy,
  author  = {Virtanen, Pauli and Gommers, Ralf and Oliphant, Travis E. and
            Haberland, Matt and Reddy, Tyler and Cournapeau, David and
            Burovski, Evgeni and Peterson, Pearu and Weckesser, Warren and
            Bright, Jonathan and {van der Walt}, St{\'e}fan J. and
            Brett, Matthew and Wilson, Joshua and Millman, K. Jarrod and
            Mayorov, Nikolay and Nelson, Andrew R. J. and Jones, Eric and
            Kern, Robert and Larson, Eric and Carey, C J and
            Polat, {\.I}lhan and Feng, Yu and Moore, Eric W. and
            {VanderPlas}, Jake and Laxalde, Denis and Perktold, Josef and
            Cimrman, Robert and Henriksen, Ian and Quintero, E. A. and
            Harris, Charles R. and Archibald, Anne M. and
            Ribeiro, Ant{\^o}nio H. and Pedregosa, Fabian and
            {van Mulbregt}, Paul and {SciPy 1.0 Contributors}},
  title   = {{{SciPy} 1.0: Fundamental Algorithms for Scientific
            Computing in Python}},
  journal = {Nat. Methods},
  year    = {2020},
  volume  = {17},
  pages   = {261--272},
}

@book{boyd2004convex,
  title={Convex optimization},
  author={Boyd, Stephen and Boyd, Stephen P and Vandenberghe, Lieven},
  year={2004},
  publisher={Cambridge university press}
}

@article{willatt_feature_2018,
  title={Feature optimization for atomistic machine learning yields a data-driven construction of the periodic table of the elements},
  author={Willatt, Michael J and Musil, F{\'e}lix and Ceriotti, Michele},
  journal={Phys. Chem. Chem. Phys.},
  volume={20},
  number={47},
  pages={29661--29668},
  year={2018},
  publisher={Royal Society of Chemistry}
}

@article{tenenbaum2000global,
 author = {Tenenbaum, J. B.},
 title = {A Global Geometric Framework for Nonlinear Dimensionality Reduction},
 journal = {Science},
 volume = {290},
 number = {5500},
 pages = {2319-2323},
 year = {2000},
 publisher = {American Association for the Advancement of Science (AAAS)},
 month = dec,
}

@article{imbalzano_automatic_2018,
	title = {Automatic selection of atomic fingerprints and reference configurations for machine-learning potentials},
	volume = {148},
	number = {24},
	journal = {J. Chem. Phys.},
	author = {Imbalzano, Giulio and Anelli, Andrea and Giofr\'e, Daniele and Klees, Sinja and Behler, J\"{o}rg and Ceriotti, Michele},
	year = {2018},
	pages = {241730},
}

@article{faber_prediction_2017,
	title = {Prediction {Errors} of {Molecular} {Machine} {Learning} {Models} {Lower} than {Hybrid} {DFT} {Error}},
	volume = {13},
	number = {11},
	journal = {J. Chem. Theory Comput.},
	author = {Faber, Felix A. and Hutchison, Luke and Huang, Bing and Gilmer, Justin and Schoenholz, Samuel S. and Dahl, George E. and Vinyals, Oriol and Kearnes, Steven and Riley, Patrick F. and von Lilienfeld, O A},
	year = {2017},
	pages = {5255--5264},
}

@book{brazdil2008metalearning,
  title={Metalearning: Applications to data mining},
  author={Brazdil, Pavel and Carrier, Christophe Giraud and Soares, Carlos and Vilalta, Ricardo},
  year={2008},
  publisher={Springer Science \& Business Media}
}