Nature Computational Science

Nature Computational Science is a multidisciplinary journal that focuses on the development and use of computational techniques and mathematical models, as well as their application to address complex problems across a range of scientific disciplines. The journal publishes both fundamental and applied research, from groundbreaking algorithms, tools and frameworks that notably help to advance scientific research, to methodologies that use computing capabilities in novel ways to find new insights and solve challenging real-world problems. By doing so, the journal creates a unique environment to bring together different disciplines to discuss the latest advances in computational science.   Disciplines covered by Nature Computational Science include, but are not limited to:

• Bioinformatics
• Cheminformatics
• Geoinformatics
• Climate Modeling and Simulation
• Computational Physics and Cosmology
• Applied Math
• Materials Science
• Urban Science and Technology
• Scientific Computing
• Methods, Tools and Platforms for Computational Science
• Visualization and Virtual Reality for Computational Science

Nature Computational Science is committed to publishing significant, high-quality research through a fair and rigorous peer-review process that is overseen by a team of full-time professional editors.

Deep learning accelerates discovery of complex nanomaterials

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Predicting physics efficiently with hybrid hardware

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A scalable tool for fast and flexible variant identification in mass spectrometry

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Decoding omics via representation learning

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SciSciGPT: advancing human–AI collaboration in the science of science

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AUTOENCODIX: a generalized and versatile framework to train and evaluate autoencoders for biological representation learning and beyond

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Gradient-based optimization of complex nanoparticle heterostructures enabled by deep learning on heterogeneous graphs

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Scouter predicts transcriptional responses to genetic perturbations with large language model embeddings

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