• The multiple disciplines (including biological sciences, physical sciences, and environmental sciences) that are covered by Nature Computational Science.

    Check out our one-year anniversary collection, in which we highlight some of the research articles, published during our first year, that reported stimulating ideas, methods and results in many different science areas, including biological sciences, physical sciences, and environmental sciences.

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.


  • A manifold learning method called T-PHATE is developed for high-dimensional time-series data. T-PHATE is applied to brain data (functional magnetic resonance imaging) where it faithfully denoises signals and unveils latent brain-state trajectories which correspond with cognitive processing.

    • Erica L. Busch
    • Jessie Huang
    • Nicholas B. Turk-Browne
  • A computational method is proposed to generate the full-scale dataset of the tridimensional position and connectivity of neurons in the CA1 region of the human hippocampus starting from high-resolution microscopy images and experimental data.

    • Daniela Gandolfi
    • Jonathan Mapelli
    • Michele Migliore
    Resource Open Access
  • A biasing energy derived from the uncertainty of a neural network ensemble modifies the potential energy surface in molecular dynamics simulations to rapidly discover under-represented structural regions that meaningfully augment the training data set.

    • Maksim Kulichenko
    • Kipton Barros
    • Benjamin Nebgen
    Article Open Access
  • A topological data analysis-driven machine learning model for guiding protein engineering is proposed, complementing protein sequence and structure embeddings when navigating the fitness landscape.

    • Yuchi Qiu
    • Guo-Wei Wei
  • The concept of evolving scattering networks is proposed for material design in wave physics. The concept has the potential to enable network-based material classification, microstructure screening and the design of stealthy hyperuniformity with superdense phases.

    • Sunkyu Yu
    Article Open Access