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Showing 1–8 of 8 results
Advanced filters: Author: Stefan Chmiela Clear advanced filters

  • Equivariant neural networks are state-of-the-art for machine learning-driven molecular dynamics (MD) simulations but have high computational cost. Here, the authors develop a Euclidean transformer that balances accuracy, stability, and speed, enabling stable long-timescale simulations of complex molecules

    • J. Thorben Frank
    • Oliver T. Unke
    • Stefan Chmiela
    ResearchOpen Access
    Nature Communications
    Volume: 15, 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

  • Machine learning is an increasingly popular approach to analyse data and make predictions. Here the authors develop a ‘deep learning’ framework for quantitative predictions and qualitative understanding of quantum-mechanical observables of chemical systems, beyond properties trivially contained in the training data.

    • Kristof T. Schütt
    • Farhad Arbabzadah
    • Alexandre Tkatchenko
    ResearchOpen Access
    Nature Communications
    Volume: 8, P: 1-8

  • 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

  • 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