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

  • 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