Huziel E. Sauceda
Huziel E. Sauceda
Verified email at fhi-berlin.mpg.de - Homepage
TitleCited byYear
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
1912017
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
1252018
Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
K Schütt, PJ Kindermans, HES Felix, S Chmiela, A Tkatchenko, KR Müller
Advances in Neural Information Processing Systems, 991-1001, 2017
992017
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
492012
Towards exact molecular dynamics simulations with machine-learned force fields
S Chmiela, HE Sauceda, KR Müller, A Tkatchenko
Nature communications 9 (1), 3887, 2018
472018
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
242013
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, 2014
142014
Advances in Neural Information Processing Systems 30
KT Schütt, PJ Kindermans, HE Sauceda, S Chmiela, A Tkatchenko, ...
Curran Associates, Inc., 2017
132017
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
92013
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, 2019
82019
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
82018
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
52019
SchNet: A continuous-filter convolutional neural network for modeling quantum interactions. NIPS 30
KT Schütt, PJ Kindermans, HE Sauceda, S Chmiela, A Tkatchenko, ...
Moustafa Dieb, Balu, Khas Ahmadi, Shah, Knyazev, Das, Goh, Derevyanko, De …, 2017
52017
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
52015
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
12019
Molecular Force Fields with Gradient-Domain Machine Learning: Dynamics of Small Molecules with Coupled Cluster Forces
H Sauceda, S Chmiela, I Poltavsky, KR Müller, A Tkatchenko
APS Meeting Abstracts, 2019
2019
Modeling Molecular Spectra with Interpretable Atomistic Neural Networks
M Gastegger, K Schütt, H Sauceda, KR Müller, A Tkatchenko
APS Meeting Abstracts, 2019
2019
Towards spectroscopic accuracy in molecular dynamics simulations with machine-learned CCSD (T) force fields
S Chmiela, H Sauceda, KR Mueller, A Tkatchenko
ABSTRACTS OF PAPERS OF THE AMERICAN CHEMICAL SOCIETY 255, 2018
2018
Structural determination of metal nanoparticles from their vibrational (phonon) density of states
I Garzon, H Sauceda
APS Meeting Abstracts, 2015
2015
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Articles 1–19