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  Machine learning of accurate energy-conserving molecular force fields

Chmiela, S., Tkatchenko, A., Sauceda, H. E., Poltavsky, I., Schütt, K. T., & Müller, K.-R. (2017). Machine learning of accurate energy-conserving molecular force fields. Science Advances, 3(5): e1603015. doi:10.1126/sciadv.1603015.

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2017 © The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. Distributed under a Creative Commons Attribution NonCommercial License 4.0 (CC BY-NC).

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 Urheber:
Chmiela, Stefan1, Autor
Tkatchenko, Alexandre1, Autor
Sauceda, Huziel E.1, Autor
Poltavsky, Igor1, Autor
Schütt, Kristof T.1, Autor
Müller, Klaus-Robert2, Autor           
Affiliations:
1External Organizations, ou_persistent22              
2Computational Biology and Applied Algorithmics, MPI for Informatics, Max Planck Society, ou_40046              

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 Zusammenfassung: Using conservation of energy - a fundamental property of closed classical and quantum mechanical systems - we develop an efficient gradient-domain machine learning (GDML) approach to construct accurate molecular force fields using a restricted number of samples from ab initio molecular dynamics (AIMD) trajectories. The GDML implementation is able to reproduce global potentialenergy surfaces of intermediate-size molecules with an accuracy of 0.3 kcal/mol-1 for energies and 1 kcal mol-1 Å̊−1 for atomic forces using only 1000 conformational geometries for training. We demonstrate this accuracy for AIMD trajectories of molecules, including benzene, toluene, naphthalene, ethanol, uracil, and aspirin. The challenge of constructing conservative force fields is accomplished in our work by learning in a Hilbert space of vector-valued functions that obey the law of energy conservation. The GDML approach enables quantitative molecular dynamics simulations for molecules at a fraction of cost of explicit AIMD calculations, thereby allowing the construction of efficient force fields with the accuracy and transferability of high-level ab initio methods.

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Sprache(n): eng - English
 Datum: 2016-12-012017-03-072017-05-05
 Publikationsstatus: Online veröffentlicht
 Seiten: 7
 Ort, Verlag, Ausgabe: -
 Inhaltsverzeichnis: -
 Art der Begutachtung: Expertenbegutachtung
 Identifikatoren: DOI: 10.1126/sciadv.1603015
BibTex Citekey: Chmiela2017
PMC: PMC5419702
 Art des Abschluß: -

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Titel: Science Advances
  Andere : Sci. Adv.
Genre der Quelle: Zeitschrift
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Affiliations:
Ort, Verlag, Ausgabe: Washington : AAAS
Seiten: 7 Band / Heft: 3 (5) Artikelnummer: e1603015 Start- / Endseite: - Identifikator: Anderer: 2375-2548
CoNE: https://pure.mpg.de/cone/journals/resource/2375-2548