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Harnessing the power of computational science

The use and development of sophisticated computing capabilities to analyse and solve real-world, challenging problems has undoubtedly revolutionized the way researchers do science. Computational science is inherently multidisciplinary, where developed techniques can have a broad impact on the scientific community. Nature Computational Science, a new journal from the Nature Research family launching January 2021, will provide a unified home for exceptional work being done in this field. To mark the impending launch of the journal, the editors have curated a collection of content to showcase how computational science is being developed and implemented in a variety of domains.

Biology, Bioengineering, and Medicine

Chemistry and Materials Science

Quantum machine learning with improved data efficiency and transferability has been achieved using on-the-fly selection of query-dependent training molecules, which are drawn from a ‘dictionary’ of atom-in-molecule-based fragments. The benefits of the resulting models have been demonstrated for important molecular properties and for systems including organic molecules, 2D materials, water clusters, DNA base pairs and ubiquitin.

Article | | Nature Chemistry

Computational chemistry has remained largely inaccessible to the experimental chemistry community. Here we report the VIRTUAL CHEMIST, a software suite free for academic use, that enables organic chemists without expertise in computational chemistry to perform virtual screening experiments for asymmetric catalyst discovery and design.

Article | | Nature Catalysis

First-principles-based multiscale models provide mechanistic insight and allow screening of large materials spaces to find promising new catalysts. In this Review, Reuter and co-workers discuss methodological cornerstones of existing approaches and highlight successes and ongoing developments in the field.

Review Article | | Nature Catalysis

Accurately predicting battery lifetime is difficult, and a prediction often cannot be made unless a battery has already degraded significantly. Here the authors report a machine-learning method to predict battery life before the onset of capacity degradation with high accuracy.

Article | | Nature Energy

Photon-induced charge separation phenomena are at the heart of light-harvesting applications but challenging to be described by quantum mechanical models. Here the authors illustrate the potential of machine-learning approaches towards understanding the fundamental processes governing electronic excitations.

Perspective | Open Access | | Nature Communications

Machine learning models insufficient for certain screening tasks can still provide valuable predictions in specific sub-domains of the considered materials. Here, the authors introduce a diagnostic tool to detect regions of low expected model error as demonstrated for the case of transparent conducting oxides.

Article | Open Access | | Nature Communications

Physics and Network Science

Rich data are revealing that complex dependencies between the nodes of a network may not be captured by models based on pairwise interactions. Higher-order network models go beyond these limitations, offering new perspectives for understanding complex systems.

Perspective | | Nature Physics

A quantum circuit-based algorithm inspired by convolutional neural networks is shown to successfully perform quantum phase recognition and devise quantum error correcting codes when applied to arbitrary input quantum states.

Article | | Nature Physics

A protocol for the reliable, efficient and precise characterization of quantum noise is reported and implemented in an architecture consisting of 14 superconducting qubits. Correlated noise within arbitrary sets of qubits can be easily detected.

Letter | | Nature Physics


Machine learning has been used to represent small-scale processes, such as clouds, in atmospheric models but this can lead to instability in simulations of climate. Here, the authors demonstrate a use of machine learning in an atmospheric model that leads to stable simulations of climate at a range of grid spacings.

Article | Open Access | | Nature Communications

One of the largest continental microplates on Earth is situated in the center of the East African Rift System, and oddly, the Victoria microplate rotates counterclockwise with respect to the neighboring African tectonic plate. Here, the authors' modelling results suggest that Victoria microplate rotation is caused by edge-driven lithospheric processes related to the specific geometry of rheologically weak and strong regions.

Article | Open Access | | Nature Communications