Volume 6 Issue 1, January 2024

More than half the world’s population lives in cities, which are hotter than rural areas. Jan Carmeliet and Dominique Derome explain what physics modelling can show about how cities get hot, and how to cool them.

Reproducibility is known to be one of the biggest issues facing science today — but what is less discussed is its connection to science’s environmental impact, as experiments that aren’t replicable still consume resources. Joanna Marshall-Cook and Martin Farley describe processes that can both improve sustainability in science and help tackle the reproducibility crisis.

Environmental concerns and rising energy costs are causing leadership of accelerator facilities to consider the impact of the magnets used. How do permanent magnets — which don’t use electricity to operate — stack up?

In 2023, the IceCube Neutrino Observatory discovered high-energy neutrinos from the Milky Way, an important clue towards understanding the origin of high-energy cosmic rays.

Non-Hermitian acoustic resonances in open systems provide a versatile platform to manipulate sound–matter interaction. This Review article surveys the fundamental physics of various acoustic resonances and their uses in realizing different acoustic wave-based applications.

Beyond in vivo models, stem cell-based in vitro models and theoretical models of morphogenesis have been constructed to recapitulate morphogenetic events during embryo development with heightened quantitative specificity. This Review discusses the accomplishments, challenges and opportunities of these models in promoting knowledge of mammalian development, including human development.

Verification efforts of density-functional theory (DFT) calculations are of crucial importance to evaluate the reliability of simulation results. In this Expert Recommendation, we suggest metrics for DFT verification, illustrating them with an all-electron reference dataset of 960 equations of state covering the whole periodic table (hydrogen to curium) and discuss the importance of improving pseudopotential codes.

Quantum learning theory is a new and very active area of research at the intersection of quantum computing and machine learning. This Perspective surveys the progress in this field, highlighting a number of exciting open questions.