Huziel E. Sauceda
Huziel E. Sauceda
Verified email at fhi-berlin.mpg.de - Homepage
Title
Cited by
Cited by
Year
Machine learning of accurate energy-conserving molecular force fields
S Chmiela, A Tkatchenko, HE Sauceda, I Poltavsky, KT Schütt, KR Müller
Science advances 3 (5), e1603015, 2017
4002017
SchNet–A deep learning architecture for molecules and materials
KT Schütt, HE Sauceda, PJ Kindermans, A Tkatchenko, KR Müller
The Journal of Chemical Physics 148 (24), 241722, 2018
3682018
Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
K Schütt, PJ Kindermans, HE Sauceda Felix, S Chmiela, A Tkatchenko, ...
Advances in neural information processing systems 30, 991-1001, 2017
2072017
Towards exact molecular dynamics simulations with machine-learned force fields
S Chmiela, HE Sauceda, KR Müller, A Tkatchenko
Nature communications 9 (1), 1-10, 2018
1702018
Vibrational properties of metal nanoparticles: Atomistic simulation and comparison with time-resolved investigation
HE Sauceda, D Mongin, P Maioli, A Crut, M Pellarin, N Del Fatti, F Vallée, ...
The Journal of Physical Chemistry C 116 (47), 25147-25156, 2012
642012
sGDML: Constructing accurate and data efficient molecular force fields using machine learning
S Chmiela, HE Sauceda, I Poltavsky, KR Müller, A Tkatchenko
Computer Physics Communications 240, 38-45, 2019
412019
Size and shape dependence of the vibrational spectrum and low-temperature specific heat of Au nanoparticles
HE Sauceda, F Salazar, LA Pérez, IL Garzón
The Journal of Physical Chemistry C 117 (47), 25160-25168, 2013
352013
Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces
HE Sauceda, S Chmiela, I Poltavsky, KR Müller, A Tkatchenko
The Journal of chemical physics 150 (11), 114102, 2019
322019
Structural determination of metal nanoparticles from their vibrational (phonon) density of states
HE Sauceda, IL Garzón
The Journal of Physical Chemistry C 119 (20), 10876-10880, 2015
272015
Advances in Neural Information Processing Systems 30
K Schütt, PJ Kindermans, HE Sauceda Felix, S Chmiela, A Tkatchenko, ...
Guyon, I., Luxburg, UV, Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S …, 2017
242017
Mechanical vibrations of atomically defined metal clusters: From nano-to molecular-size oscillators
P Maioli, T Stoll, HE Sauceda, I Valencia, A Demessence, F Bertorelle, ...
Nano Letters 18 (11), 6842-6849, 2018
232018
Vibrational Spectrum, Caloric Curve, Low-Temperature Heat Capacity, and Debye Temperature of Sodium Clusters: The Na139+ Case
HE Sauceda, JJ Pelayo, F Salazar, LA Pérez, IL Garzón
The Journal of Physical Chemistry C 117 (21), 11393-11398, 2013
182013
Construction of machine learned force fields with quantum chemical accuracy: Applications and chemical insights
HE Sauceda, S Chmiela, I Poltavsky, KR Müller, A Tkatchenko
arXiv preprint arXiv:1909.08565, 2019
82019
Vibrational properties and specific heat of core–shell Ag–Au icosahedral nanoparticles
HE Sauceda, IL Garzón
Physical Chemistry Chemical Physics 17 (42), 28054-28059, 2015
82015
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions. NIPS 30
KT Schütt, PJ Kindermans, HE Sauceda, S Chmiela, A Tkatchenko, ...
Zeng, Tahaei, Chen, Meister, Shah, Gupta, Jalal, Arvaniti, Zimmerer …, 2017
62017
Accurate Molecular Dynamics Enabled by Efficient Physically Constrained Machine Learning Approaches
S Chmiela, HE Sauceda, A Tkatchenko, KR Müller
Machine Learning Meets Quantum Physics, 129-154, 2020
32020
Dynamical strengthening of covalent and non-covalent molecular interactions by nuclear quantum effects at finite temperature
HE Sauceda, V Vassilev-Galindo, S Chmiela, KR Müller, A Tkatchenko
arXiv preprint arXiv:2006.10578, 2020
22020
Modeling molecular spectra with interpretable atomistic neural networks
M Gastegger, K Schütt, H Sauceda, KR Müller, A Tkatchenko
APS 2019, E32. 007, 2019
22019
Molecular force fields with gradient-domain machine learning (GDML): Comparison and synergies with classical force fields
HE Sauceda, M Gastegger, S Chmiela, KR Müller, A Tkatchenko
The Journal of Chemical Physics 153 (12), 124109, 2020
12020
Machine Learning Force Fields
OT Unke, S Chmiela, HE Sauceda, M Gastegger, I Poltavsky, KT Schütt, ...
arXiv preprint arXiv:2010.07067, 2020
2020
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