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Showing 1–6 of 6 results
Advanced filters: Author: Huziel E. Sauceda Clear advanced filters

  • Most machine-learning force fields dismiss long-range interactions. Here the authors demonstrate the BIGDML approach for building materials’ potential energy surfaces that enables a broad range of materials simulations within accuracies better than 1 meV/atom using just 10–200 structures for training.

    • Huziel E. Sauceda
    • Luis E. Gálvez-González
    • Alexandre Tkatchenko
    ResearchOpen Access
    Nature Communications
    Volume: 13, P: 1-16

  • The inclusion of nuclear quantum effects (NQE) in atomistic simulations of chemical systems is of key importance. Here the authors use machine learned force fields trained on coupled cluster reference data to show the dynamical strengthening of covalent and non-covalent molecular interactions induced by NQE.

    • Huziel E. Sauceda
    • Valentin Vassilev-Galindo
    • Alexandre Tkatchenko
    ResearchOpen Access
    Nature Communications
    Volume: 12, P: 1-10

  • Band engineering in optics allows the design of unconventional forms of light with potential optoelectronic applications. Here, the authors realize slow-light intercavity polaritons in an array of coupled cavities, the photonic architecture enables the spatial segregation of photons and excitons

    • Yesenia A. García Jomaso
    • Brenda Vargas
    • Giuseppe Pirruccio
    ResearchOpen Access
    Nature Communications
    Volume: 15, P: 1-8

  • Current machine-learned force fields typically ignore electronic degrees of freedom. SpookyNet is a deep neural network that explicitly treats electronic degrees of freedom, closing an important remaining gap for models in quantum chemistry.

    • Oliver T. Unke
    • Stefan Chmiela
    • Klaus-Robert Müller
    ResearchOpen Access
    Nature Communications
    Volume: 12, P: 1-14

  • Simultaneous accurate and efficient prediction of molecular properties relies on combined quantum mechanics and machine learning approaches. Here the authors develop a flexible machine-learning force-field with high-level accuracy for molecular dynamics simulations.

    • Stefan Chmiela
    • Huziel E. Sauceda
    • Alexandre Tkatchenko
    ResearchOpen Access
    Nature Communications
    Volume: 9, P: 1-10

  • The vibrational properties of fullerenes are incompletely understood, particularly with respect to the effect of molecular size. Here the vibrational density of states of fullerenes is shown by density functional theory to converge smoothly to that of graphene, hindered only by the presence of frequency compressed radial optic vibrations due to the pentagonal faces in the fullerene family.

    • Jesús N. Pedroza-Montero
    • Ignacio L. Garzón
    • Huziel E. Sauceda
    ResearchOpen Access
    Communications Chemistry
    Volume: 4, P: 1-8