Reference : Machine learning of accurate energy-conserving molecular force fields
Scientific journals : Article
Physical, chemical, mathematical & earth Sciences : Mathematics
Physics and Materials Science; Computational Sciences
http://hdl.handle.net/10993/31098
Machine learning of accurate energy-conserving molecular force fields
English
Chmiela, Stefan [> >]
Tkatchenko, Alexandre mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Physics and Materials Science Research Unit >]
Sauceda, Huziel [> >]
Poltasvkyi, Igor mailto [University of Luxembourg > Faculty of Science, Technology and Communication (FSTC) > Physics and Materials Science Research Unit >]
Schuett, Kristof [> >]
Mueller, Klaus-Robert [> >]
2017
Science Advances
AAAS
3
e1603015
Yes
International
[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.
http://hdl.handle.net/10993/31098

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