Difference between revisions of "Template:Reference list"

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== Reference list ==
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<references>
 
<references>
 
<ref name="bartok_2010">
 
<ref name="bartok_2010">
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<ref name="muhli_2021b">
 
<ref name="muhli_2021b">
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'''H. Muhli, X. Chen, A.P. Bartók, P. Hernández-León, G. Csányi, T. Ala-Nissila, and M.A. Caro'''. ''Machine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of C<sub>60</sub>''. [https://arxiv.org/abs/2105.02525 arXiv preprint arXiv:2105.02525 (2021)].
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'''H. Muhli, X. Chen, A.P. Bartók, P. Hernández-León, G. Csányi, T. Ala-Nissila, and M.A. Caro'''. ''Machine learning force fields based on local parametrization of dispersion interactions: Application to the phase diagram of C<sub>60</sub>''. [https://journals.aps.org/prb/abstract/10.1103/PhysRevB.104.054106 Phys. Rev. B '''104''', 054106 (2021)].
 
</ref>
 
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'''A.P. Bartók and G. Csányi'''. ''Gaussian Approximation Potentials: a brief tutorial introduction''. [https://doi.org/10.1002/qua.24927 Int. J. Quantum Chem. '''115''', 1051 (2015)].
 
'''A.P. Bartók and G. Csányi'''. ''Gaussian Approximation Potentials: a brief tutorial introduction''. [https://doi.org/10.1002/qua.24927 Int. J. Quantum Chem. '''115''', 1051 (2015)].
 
</ref>
 
</ref>
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<ref name="caro_2019">
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'''M.A. Caro'''. ''Optimizing many-body atomic descriptors for enhanced computational performance of machine learning based interatomic potentials''. [https://journals.aps.org/prb/abstract/10.1103/PhysRevB.100.024112 Phys. Rev. B '''100''', 024112 (2019)].
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</ref>
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<ref name="tkatchenko_2009">
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'''A. Tkatchenko and M. Scheffler'''. ''Accurate Molecular Van Der Waals Interactions from Ground-State Electron Density and Free-Atom Reference Data''.
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[https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.102.073005 Phys. Rev. Lett. '''102''', 073005 (2009)].
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</ref>
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<ref name="tkatchenko_2012">
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'''A. Tkatchenko, R.A. DiStasio Jr, R. Car, and M. Scheffler'''. ''Accurate and efficient method for many-body van der Waals interactions''.
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[https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.108.236402 Phys. Rev. Lett. '''108''', 236402 (2012)].
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</ref>
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<ref name="bartok_2018">
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'''A.P. Bartók, J. Kermode, N. Bernstein, and G. Csányi'''. ''Machine learning a general-purpose interatomic potential for silicon''. [https://journals.aps.org/prx/abstract/10.1103/PhysRevX.8.041048 Phys. Rev. X '''8''', 041048 (2018)].
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</ref>
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<ref name="bartok_2017">
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'''A.P. Bartók, S. De, C. Poelking, N. Bernstein, J.R. Kermode, G. Csányi, and M. Ceriotti'''. ''Machine learning unifies the modeling of materials and molecules''. [https://www.science.org/doi/10.1126/sciadv.1701816 Sci. Adv. '''3''', e1701816 (2017)].
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</ref>
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<ref name="de_2016">
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'''S. De, A.P. Bartók, G. Csányi, and M. Ceriotti'''. ''Comparing molecules and solids across structural and alchemical space''. [https://pubs.rsc.org/en/content/articlelanding/2016/cp/c6cp00415f Phys. Chem. Chem. Phys. '''18''', 13754 (2016)].
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</ref>
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<ref name="hernandez-leon_2023">
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'''P. Hernández-León and M.A. Caro'''. ''Cluster-based multidimensional scaling embedding tool for data visualization''. [https://arxiv.org/abs/2209.06614 arXiv:2209.06614 (2023)].
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</ref>
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<ref name="kloppenburg_2023">
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'''J. Kloppenburg, L.B. Pártay, H. Jónsson, and M.A. Caro'''. ''A general-purpose machine learning Pt interatomic potential for an accurate description of bulk, surfaces, and nanoparticles''. [https://pubs.aip.org/aip/jcp/article/158/13/134704/2883270 J. Chem. Phys. '''158''', 134704 (2023)].
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</ref>
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</references>
 
</references>

Latest revision as of 12:08, 15 August 2023

Reference list

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