Nuclear quantum effects on molecules and materials
Path integral molecular dynamics
Design and application of molecular force-fields based on machine learning
Machine Learning
Knowledge Fields
Nanosciences and Condensed Matter
Applied Physics and Interdisciplinary Topics
Knowledge Areas
Physical and chemical properties of clusters and nanoparticles
Optical
Two-dimensional systems and interfaces
Computer Nanoscience
Interdisciplinary topics
Grants/Projects
Construcción de campos de fuerzas moleculares basados en machine learning, 2023-2023, Dirección General de Asuntos del Personal Académico,
Selected Articles
Stefan Chmiela, Valentin Vassilev-Galindo, Oliver T. Unke, Adil Kabylda, Huziel E. Sauceda, Alexandre Tkatchenko, Klaus-Robert Müller, Accurate Global Machine Learning Force Fields For Molecules With Hundreds Of Atoms, Science Advances, January 2023; 9(2), 11, DOI: 10.1126/sciadv.adf0873, Link
Huziel E Sauceda, Luis E Gálvez-González, Stefan Chmiela, Lauro Oliver Paz-Borbón, Klaus-Robert Müller, Alexandre Tkatchenko, Bigdml: Towards Accurate Quantum Machine Learning Force Fields For Materials, Nature Communications, January 2022; 13(1), 1-16, DOI: 10.1038/s41467-022-31093-x, Link
Ludwig Winkler, Klaus-Robert Müller, Huziel E Sauceda, High Fidelity Molecular Dynamics Trajectory Reconstruction With Bi Directional Neural Networks, Machine Learning: Science and Technology, January 2022; 3, 025011, DOI: 10.1088/2632-2153/ac6ec6, Link
Oliver T Unke, Stefan Chmiela, Huziel E Sauceda, Michael Gastegger, Igor Poltavsky, Kristof T Schütt, Alexandre Tkatchenko, Klaus-Robert Müller, Machine Learning Force Fields, Chemical Reviews, March 2021; 121(16), 45, DOI: 10.1021/acs.chemrev.0c01111, Link
Huziel E Sauceda, Valentin Vassilev-Galindo, Stefan Chmiela, Klaus-Robert Müller, Alexandre Tkatchenko, Dynamical Strengthening Of Covalent And Non Covalent Molecular Interactions By Nuclear Quantum Effects At Finite Temperature, Nature Communications, January 2021; 12(442), 10, DOI: 10.1038/s41467-020-20212-1, Link
Latest Articles
Bipeng Wang, Ludwig Winkler, Yifan Wu, Klaus-Robert Müller, Huziel E. Sauceda, Oleg V. Prezhdo, Interpolating Nonadiabatic Molecular Dynamics Hamiltonian With Bidirectional Long Short Term Memory Networks, Journal of Physical Chemistry Letters, November 2023; 14(31), 7092-7099, DOI: 10.1021/acs.jpclett.3c01723, Link
Stefan Chmiela, Valentin Vassilev-Galindo, Oliver T. Unke, Adil Kabylda, Huziel E. Sauceda, Alexandre Tkatchenko, Klaus-Robert Müller, Accurate Global Machine Learning Force Fields For Molecules With Hundreds Of Atoms, Science Advances, January 2023; 9(2), 11, DOI: 10.1126/sciadv.adf0873, Link
Huziel E Sauceda, Luis E Gálvez-González, Stefan Chmiela, Lauro Oliver Paz-Borbón, Klaus-Robert Müller, Alexandre Tkatchenko, Bigdml: Towards Accurate Quantum Machine Learning Force Fields For Materials, Nature Communications, January 2022; 13(1), 1-16, DOI: 10.1038/s41467-022-31093-x, Link
Jesús N Pedroza-Montero, Ignacio L Garzón, Huziel E Sauceda, Size Evolution Of Characteristic Acoustic Oscillations Of Fullerenes And Its Connection To Continuum Elasticity Theory, European Journal of Physics, January 2022; 76(7), 1-6, DOI: 10.1140/epjd/s10053-022-00449-9, Link
Ludwig Winkler, Klaus-Robert Müller, Huziel E Sauceda, High Fidelity Molecular Dynamics Trajectory Reconstruction With Bi Directional Neural Networks, Machine Learning: Science and Technology, January 2022; 3, 025011, DOI: 10.1088/2632-2153/ac6ec6, Link
Awards
SNI 1, Distinción, 2016
Videos
Towards predictive and interpretable machine learning models in chemistry and materials science [Español], 2022 Link
Machine learning in molecular systems and materials science: Predictive simulations [Español], 2021 Link
On the dynamical strengthening of intra/inter molecular interactions by nuclear quantum effects [English], 2021 Link
Towards predictive machine learned models in physics and chemistry [Español], 2020 Link