Browsing by Author "Noé, Frank"

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  • Machine learning of coarse-grained molecular dynamics force fields
    Wang, Jiang; Olsson, Simon; Wehmeyer, Christoph; Pérez, Adrià; Charron, Nicholas E.; De Fabritiis, Gianni; Noé, Frank; Clementi, Cecilia (American Chemical Society (ACS), 2019)
    Atomistic or ab initio molecular dynamics simulations are widely used to predict thermodynamics and kinetics and relate them to molecular structure. A common approach to go beyond the time- and length-scales accessible ...
  • TorchMD: a deep learning framework for molecular simulations
    Doerr, Stefan, 1987-; Majewski, Maciej; Pérez, Adrià; Krämer, Andreas; Clementi, Cecilia; Noé, Frank; Giorgino, Toni; De Fabritiis, Gianni (American Chemical Society (ACS), 2021)
    Molecular dynamics simulations provide a mechanistic description of molecules by relying on empirical potentials. The quality and transferability of such potentials can be improved leveraging data-driven models derived ...