Publikationen von Huziel E. Sauceda

Pedroza-Montero, J. N.; Garzón, I. L.; Sauceda, H. E.: On the forbidden graphene’s ZO (out-of-plane optic) phononic band-analog vibrational modes in fullerenes. Communications Chemistry 4, 103 (2021)

Chmiela, S.; Sauceda, H. E.; Poltavsky, I.; Müller, K.-R.; Tkatchenko, A.: sGDML: Constructing accurate and data efficient molecular force fields using machine learning. Computer Physics Communications 240, S. 38 - 45 (2019)

Sauceda, H. E.; Chmiela, S.; Poltavsky, I.; Müller, K.-R.; Tkatchenko, A.: 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.; Stoll, T.; Sauceda, H. E.; Valencia, I.; Demessence, A.; Bertorelle, F.; Crut, A.; Vallée, F.; Garzón, I. L.; Cerullo, G. et al.; Del Fatti, N.: Mechanical Vibrations of Atomically Defined Metal Clusters: From Nano- to Molecular-Size Oscillators. Nano Letters 18 (11), S. 6842 - 6849 (2018)

Chmiela, S.; Sauceda, H. E.; Müller, K.-R.; Tkatchenko, A.: Towards exact molecular dynamics simulations with machine-learned force fields. Nature Communications 9 (1), 3887 (2018)

Schütt, K. T.; Sauceda, H. E.; Kindermans, P.-J.; Tkatchenko, A.; Müller, K.-R.: SchNet – A deep learning architecture for molecules and materials. The Journal of Chemical Physics 148 (24), 241722 (2018)

Chmiela, S.; Tkatchenko, A.; Sauceda, H. E.; Poltavsky, I.; Schütt, K. T.; Müller, K.-R.: Machine learning of accurate energy-conserving molecular force fields. Science Advances 3 (5), e1603015 (2017)

Sauceda, H. E.; Chmiela, S.; Poltavsky, I.; Müller, K.-R.; Tkatchenko, A.: Construction of Machine Learned Force Fields with Quantum Chemical Accuracy: Applications and Chemical Insights. In: Machine Learning Meets Quantum Physics, S. 277 - 307 (Hg. Schütt, K. T.; Chmiela, S.; von Lilienfeld, O. A.; Tkatchenko, A.; Tsuda, K. et al.). Springer, Cham (2020)

Schütt, K. T.; Kindermans, P.-J.; Sauceda, H. E.; Chmiela, S.; Tkatchenko, A.; Müller, K.-R.: SchNet: A continuous-filter convolutional neural network for modeling quantum interactions. In: Advances in Neural Information Processing Systems, Bd. 30, S. 992 - 1002 (Hg. von Luxburg, U.). 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA, 04. Dezember 2017 - 09. Dezember 2017. Neural Information Processing Systems (NIPS) Foundation, La Jolla, CA (2018)