Nature Computational Science is a Transformative Journal; authors can publish using the traditional publishing route OR via immediate gold Open Access.

Our Open Access option complies with funder and institutional requirements.


    • An adaptive and computationally efficient machine-learning-based biasing technique for rare-event sampling is introduced, allowing an effective generation of high-dimensional free energy surfaces associated with complex processes, such as protein folding.

      • Mark E. Tuckerman
      News & Views
    • The accurate determination of correlation energy is a challenging task in many-electron quantum chemistry calculations, especially for metals. A recent work proposes an efficient scheme to speed up the calculation of correlation energy, reducing the computational time by up to two orders of magnitude.

      • Jianwei Sun
      News & Views
    • A new study proposes a full-scale model of the entorhinal cortex–dentate gyrus–CA3 network, providing a conceptual overview of the computational properties of this brain network, to show that it is an efficient pattern separator.

      • Ad Aertsen
      News & Views
    • A human mobility model that takes into account social interaction and long-term memory mechanisms is proposed, shedding more light onto the interplay between human movements and urban growth.

      • Pu Wang
      News & Views
    • Compressing scientific data is essential to save on storage space, but doing so effectively while ensuring that the conclusions from the data are not affected remains a challenging task. A recent paper proposes a new method to identify numerical noise from floating-point atmospheric data, which can lead to a more effective compression.

      • Dorit M. Hammerling
      • Allison H. Baker
      News & Views