Publications of Huziel E. Sauceda

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).

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).

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).

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).

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).

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).

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).

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.): U. von Luxburg. Neural Information Processing Systems (NIPS) Foundation, La Jolla, CA, 992–1002 (2018).