Publications of Huziel E. Sauceda

Journal Article (7)

2021
Journal Article
Pedroza-Montero, Jesús N., Ignacio L. Garzón and Huziel E. Sauceda: On the forbidden graphene’s ZO (out-of-plane optic) phononic band-analog vibrational modes in fullerenes.
2019
Journal Article
Chmiela, Stefan, Huziel E. Sauceda, Igor Poltavsky, Klaus-Robert Müller and Alexandre Tkatchenko: sGDML: Constructing accurate and data efficient molecular force fields using machine learning.
Journal Article
Sauceda, Huziel E., Stefan Chmiela, Igor Poltavsky, Klaus-Robert Müller and Alexandre Tkatchenko: Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces.
2018
Journal Article
Chmiela, Stefan, Huziel E. Sauceda, Klaus-Robert Müller and Alexandre Tkatchenko: Towards exact molecular dynamics simulations with machine-learned force fields.
Journal Article
Maioli, Paolo, Tatjana Stoll, Huziel E. Sauceda, Israel Valencia, Aude Demessence, Franck Bertorelle, Aurélien Crut, Fabrice Vallée, Ignacio L. Garzón, Giulio Cerullo and Natalia Del Fatti: Mechanical Vibrations of Atomically Defined Metal Clusters: From Nano- to Molecular-Size Oscillators.
Journal Article
Schütt, K.T., Huziel E. Sauceda, P.-J. Kindermans, Alexandre Tkatchenko and Klaus-Robert Müller: SchNet – A deep learning architecture for molecules and materials.
2017
Journal Article
Chmiela, Stefan, Alexandre Tkatchenko, Huziel E. Sauceda, Igor Poltavsky, Kristof T. Schütt and Klaus-Robert Müller: Machine learning of accurate energy-conserving molecular force fields.

Book Chapter (1)

2020
Book Chapter
Sauceda, Huziel E., Stefan Chmiela, Igor Poltavsky, Klaus-Robert Müller and Alexandre Tkatchenko: Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights.

Conference Paper (1)

2018
Conference Paper
Schütt, K.T., P.-J. Kindermans, Huziel E. Sauceda, S. Chmiela, Alexandre Tkatchenko and Klaus-Robert Müller: SchNet: A continuous-filter convolutional neural network for modeling quantum interactions.
(31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA, Dec 2017).
Go to Editor View