Towards exact molecular dynamics simulations with machine-learned force fields.
Stefan Chmiela,Huziel E. Sauceda,Klaus-Robert Müller,Klaus-Robert Müller,Klaus-Robert Müller,Alexandre Tkatchenko +5 more
TLDR
A flexible machine-learning force-field with high-level accuracy for molecular dynamics simulations is developed, for flexible molecules with up to a few dozen atoms and insights into the dynamical behavior of these molecules are provided.Abstract:
Molecular dynamics (MD) simulations employing classical force fields constitute the cornerstone of contemporary atomistic modeling in chemistry, biology, and materials science. However, the predictive power of these simulations is only as good as the underlying interatomic potential. Classical potentials often fail to faithfully capture key quantum effects in molecules and materials. Here we enable the direct construction of flexible molecular force fields from high-level ab initio calculations by incorporating spatial and temporal physical symmetries into a gradient-domain machine learning (sGDML) model in an automatic data-driven way. The developed sGDML approach faithfully reproduces global force fields at quantum-chemical CCSD(T) level of accuracy and allows converged molecular dynamics simulations with fully quantized electrons and nuclei. We present MD simulations, for flexible molecules with up to a few dozen atoms and provide insights into the dynamical behavior of these molecules. Our approach provides the key missing ingredient for achieving spectroscopic accuracy in molecular simulations.read more
Citations
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Machine learning and the physical sciences
Giuseppe Carleo,J. Ignacio Cirac,Kyle Cranmer,Laurent Daudet,Maria Schuld,Naftali Tishby,Leslie Vogt-Maranto,Lenka Zdeborová +7 more
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Unmasking Clever Hans Predictors and Assessing What Machines Really Learn
Sebastian Lapuschkin,Stephan Wäldchen,Alexander Binder,Grégoire Montavon,Wojciech Samek,Klaus-Robert Müller,Klaus-Robert Müller,Klaus-Robert Müller +7 more
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PhysNet: A Neural Network for Predicting Energies, Forces, Dipole Moments, and Partial Charges.
Oliver T. Unke,Markus Meuwly +1 more
TL;DR: The PhysNet-PES model as discussed by the authors predicts energy, forces, and dipole moments of chemical systems using deep neural networks (DNNs) and achieves state-of-the-art performance.
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From DFT to machine learning: recent approaches to materials science–a review
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Proceedings Article
Directional Message Passing for Molecular Graphs
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References
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