Article (Scientific journals)
Machine learning of accurate energy-conserving molecular force fields
Chmiela, Stefan; Tkatchenko, Alexandre; Sauceda, Huziel et al.
2017In Science Advances, 3, p. 1603015
Peer reviewed
 

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Abstract :
[en] 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 potential energy surfaces of intermediate-sized molecules with an accuracy of 0.3 kcal mol−1 for energies and 1 kcal mol−1 Å−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.
Disciplines :
Mathematics
Author, co-author :
Chmiela, Stefan
Tkatchenko, Alexandre ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Physics and Materials Science Research Unit
Sauceda, Huziel
Poltavskyi, Igor ;  University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Physics and Materials Science Research Unit
Schuett, Kristof
Mueller, Klaus-Robert
External co-authors :
yes
Language :
English
Title :
Machine learning of accurate energy-conserving molecular force fields
Publication date :
2017
Journal title :
Science Advances
Publisher :
AAAS
Volume :
3
Pages :
e1603015
Peer reviewed :
Peer reviewed
Focus Area :
Physics and Materials Science
Computational Sciences
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