Publications of Huziel E. Sauceda

J.N. Pedroza-Montero, 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).

S. Chmiela, 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).

H.E. Sauceda, 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).

S. Chmiela, 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).

P. Maioli, 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).

K.T. Schütt, 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).

S. Chmiela, 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).

H.E. Sauceda, 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.): K.T. Schütt, S. Chmiela, O.A. von Lilienfeld, A. Tkatchenko, K. Tsuda, and K.-R. Müller. (Lecture Notes in Physics). Springer, Cham, 277–307 (2020).

K.T. Schütt, 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).