Publications of Huziel E. Sauceda

Journal Article (7)

2021
Journal Article
Pedroza-Montero, J.N., I.L. Garzón and H.E. Sauceda: On the forbidden graphene’s ZO (out-of-plane optic) phononic band-analog vibrational modes in fullerenes. Communications Chemistry 4, 103 (2021).
2019
Journal Article
Chmiela, S., H.E. Sauceda, I. Poltavsky, K.-R. Müller and A. Tkatchenko: sGDML: Constructing accurate and data efficient molecular force fields using machine learning. Computer Physics Communications 240, 38–45 (2019).
Journal Article
Sauceda, H.E., S. Chmiela, I. Poltavsky, K.-R. Müller and A. Tkatchenko: Molecular force fields with gradient-domain machine learning: Construction and application to dynamics of small molecules with coupled cluster forces. The Journal of Chemical Physics 150 (11), 114102 (2019).
2018
Journal Article
Chmiela, S., H.E. Sauceda, K.-R. Müller and A. Tkatchenko: Towards exact molecular dynamics simulations with machine-learned force fields. Nature Communications 9 (1), 3887 (2018).
Journal Article
Maioli, P., T. Stoll, H.E. Sauceda, I. Valencia, A. Demessence, F. Bertorelle, A. Crut, F. Vallée, I.L. Garzón, G. Cerullo and N. Del Fatti: Mechanical Vibrations of Atomically Defined Metal Clusters: From Nano- to Molecular-Size Oscillators. Nano Letters 18 (11), 6842–6849 (2018).
Journal Article
Schütt, K.T., H.E. Sauceda, P.-J. Kindermans, A. Tkatchenko and K.-R. Müller: SchNet – A deep learning architecture for molecules and materials. The Journal of Chemical Physics 148 (24), 241722 (2018).
2017
Journal Article
Chmiela, S., A. Tkatchenko, H.E. Sauceda, I. Poltavsky, K.T. Schütt and K.-R. Müller: Machine learning of accurate energy-conserving molecular force fields. Science Advances 3 (5), e1603015 (2017).

Book Chapter (1)

2020
Book Chapter
Sauceda, H.E., S. Chmiela, I. Poltavsky, K.-R. Müller and A. Tkatchenko: Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights. In: Machine Learning Meets Quantum Physics. (Eds.): <span style="font-style: normal; font-weight: normal"><span class='author_name'>K.T. Schütt</span></span> <span style="font-style: normal; font-weight: normal"><span class='author_name'>S. Chmiela</span></span> <span style="font-style: normal; font-weight: normal"><span class='author_name'>O.A. von Lilienfeld</span></span> <span style="font-style: normal; font-weight: normal"><span class='author_name'>A. Tkatchenko</span></span> <span style="font-style: normal; font-weight: normal"><span class='author_name'>K. Tsuda</span></span> and <span style="font-style: normal; font-weight: normal"><span class='author_name'>K.-R. Müller</span></span> (Lecture Notes in Physics, Vol. 968). Springer, Cham, 277–307 (2020).

Conference Paper (1)

2018
Conference Paper
Schütt, K.T., P.-J. Kindermans, H.E. Sauceda, S. Chmiela, A. Tkatchenko and K.-R. Müller: SchNet: A continuous-filter convolutional neural network for modeling quantum interactions. In: Advances in Neural Information Processing Systems. (Ed.): <span style="font-style: normal; font-weight: normal"><span class='author_name'>U. von Luxburg</span></span> Neural Information Processing Systems (NIPS) Foundation, La Jolla, CA, 992–1002 (2018).
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