Our People


Huziel Enoc Sauceda Felix
Research Associate
Affiliation: Condensed Matter

Building : Marcos Moshinsky, Cubículo 147
Phone: 5556225000 ext 2405

Website



Research Lines

  • Deep Learning
  • 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