Volume 4 Issue 6, June 2022

Topological defects play an important role in biology, as shown by a growing body of evidence. Aleksandra Ardaševa and Amin Doostmohammadi survey the new research directions that are opening.

Over the past decade machine learning has made significant advances in approximating density functionals, but whether this signals the end of human-designed functionals remains to be seen.

Giulia Menichetti describes a deep learning pipeline designed to provide reliable binding predictions for poorly annotated proteins and ligands.

A paper in Physical Review Letters uses experiments and simulations to understand the mechanical properties of bird nests.

Sonja Franke-Arnold discusses the first experimental generation of light with orbital angular momentum three decades ago and outlines the subsequent advances.

An article in Physical Reviews X shows that quantum correlations can enhance the expressivity of generative models, suggesting new ways to develop improved (quantum-inspired) classical machine learning methods.

Minimizing the energy of the Ising model is a prototypical combinatorial optimization problem, ubiquitous in our increasingly automated world. This Review surveys Ising machines — special-purpose hardware solvers for this problem — and examines the various operating principles and compares their performance.

Active matter encompasses various non-equilibrium systems in which individual constituents convert energy into non-conservative forces or motion at the microscale. This Review provides an elementary introduction to the role of topology in active matter through experimentally relevant examples.

Owing to the growing volumes of data from high-energy physics experiments, modern deep learning methods are playing an increasingly important role in all aspects of data taking and analysis. This Review provides an overview of key developments, with a focus on the search for physics beyond the standard model.

Finding the most appropriate machine learning algorithm for the analysis of any given scientific dataset is currently challenging, but new machine learning benchmarks for science are being developed to help.