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
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
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
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
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