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Huziel E. Sauceda
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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), 2018
16472018
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
11452017
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, 2017
10912017
Machine learning force fields
OT Unke, S Chmiela, HE Sauceda, M Gastegger, I Poltavsky, KT Schütt, ...
Chemical Reviews 121 (16), 10142-10186, 2021
7722021
Towards exact molecular dynamics simulations with machine-learned force fields
S Chmiela, HE Sauceda, KR Müller, A Tkatchenko
Nature communications 9 (1), 3887, 2018
6592018
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
2022019
SpookyNet: Learning force fields with electronic degrees of freedom and nonlocal effects
OT Unke, S Chmiela, M Gastegger, KT Schütt, HE Sauceda, KR Müller
Nature communications 12 (1), 7273, 2021
1902021
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), 2019
1112019
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
892012
Advances in Neural Information Processing Systems
K Schütt, PJ Kindermans, HES Felix, S Chmiela, A Tkatchenko, KR Müller
arXiv preprint arXiv:1706.08566, 991-1001, 2017
702017
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
652018
Accurate global machine learning force fields for molecules with hundreds of atoms
S Chmiela, V Vassilev-Galindo, OT Unke, A Kabylda, HE Sauceda, ...
Science Advances 9 (2), eadf0873, 2023
602023
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
492013
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
462015
BIGDML—Towards accurate quantum machine learning force fields for materials
HE Sauceda, LE Gálvez-González, S Chmiela, LO Paz-Borbón, KR Müller, ...
Nature communications 13 (1), 3733, 2022
442022
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), 2020
382020
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), 442, 2021
362021
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
212013
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
172020
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
112020
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