Huziel E. Sauceda
Title
Cited by
Cited by
Year
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
6152018
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
5742017
Schnet: A continuous-filter convolutional neural network for modeling quantum interactions
KT Schütt, PJ Kindermans, HE Sauceda, S Chmiela, A Tkatchenko, ...
arXiv preprint arXiv:1706.08566, 2017
3582017
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
2912018
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
752019
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
682012
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
532019
Machine learning force fields
OT Unke, S Chmiela, HE Sauceda, M Gastegger, I Poltavsky, KT Schütt, ...
Chemical Reviews, 2021
492021
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
422018
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
392013
Advances in Neural Information Processing Systems 30
KT Schütt, PJ Kindermans, HE Sauceda, S Chmiela, A Tkatchenko, ...
Guyon, I., Luxburg, UV, Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S …, 2017
342017
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
292015
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
202013
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
Nature Communications 12 (1), 1-10, 2021
132021
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
102020
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
102015
Construction of machine learned force fields with quantum chemical accuracy: Applications and chemical insights
HE Sauceda, S Chmiela, I Poltavsky, KR Müller, A Tkatchenko
Machine Learning Meets Quantum Physics, 277-307, 2020
92020
Spookynet: Learning force fields with electronic degrees of freedom and nonlocal effects
OT Unke, S Chmiela, M Gastegger, KT Schütt, HE Sauceda, KR Müller
arXiv preprint arXiv:2105.00304, 2021
52021
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
42020
Modeling molecular spectra with interpretable atomistic neural networks
M Gastegger, K Schütt, H Sauceda, KR Müller, A Tkatchenko
APS March Meeting Abstracts 2019, E32. 007, 2019
22019
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Articles 1–20