References of "Sauceda, Huziel E"
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See detailMolecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields
Sauceda, Huziel E; Gastegger, Michael; Chmiela, Stefan et al

in Journal of Chemical Physics (2020)

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See detailTowards exact molecular dynamics simulations with machine-learned force fields
Chmiela, Stefan; Sauceda, Huziel E.; Müller, Klaus-Robert et al

in Nature Communications (2018), 9

Molecular dynamics (MD) simulations employing classical force fields constitute the cornerstone of contemporary atomistic modeling in chemistry, biology, and materials science. However, the predictive ... [more ▼]

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. [less ▲]

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See detailSchNet – A deep learning architecture for molecules and materials
Schütt, Kristof T.; Sauceda, Huziel E.; Kindermans, P. J. et al

in Journal of Chemical Physics (2018), 148

Deep learning has led to a paradigm shift in artificial intelligence, including web, text, and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics. Machine ... [more ▼]

Deep learning has led to a paradigm shift in artificial intelligence, including web, text, and image search, speech recognition, as well as bioinformatics, with growing impact in chemical physics. Machine learning, in general, and deep learning, in particular, are ideally suitable for representing quantum-mechanical interactions, enabling us to model nonlinear potential-energy surfaces or enhancing the exploration of chemical compound space. Here we present the deep learning architecture SchNet that is specifically designed to model atomistic systems by making use of continuous-filter convolutional layers. We demonstrate the capabilities of SchNet by accurately predicting a range of properties across chemical space for molecules and materials, where our model learns chemically plausible embeddings of atom types across the periodic table. Finally, we employ SchNet to predict potential-energy surfaces and energy-conserving force fields for molecular dynamics simulations of small molecules and perform an exemplary study on the quantum-mechanical properties of C20- fullerene that would have been infeasible with regular ab initio molecular dynamics. [less ▲]

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See detailSchNet: A continuous-filter convolutional neural network for modeling quantum interactions
Schütt, Kristof T.; Kindermans, P. J.; Sauceda, Huziel E. et al

in 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA (2017, December)

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