Reviews & Analysis

A new attention mechanism brings long-range awareness to machine learning force fields with linear cost and preservation of symmetry. The method offers a flexible alternative to existing long-range modules, including fragmentation-based interactions and physics-based long-range fixes.

A new machine learning framework predicts the spin–orbit-coupled electronic structure across the periodic table, enabling high-throughput exploration of quantum materials.

Neural networks may be overconfident before they see real data. By briefly training on random noise, models can learn to be uncertain, leading to better calibration, improved identification of out-of-distribution inputs and thus more reliable predictions.

Combining soft robotics with neuromorphic engineering is a promising approach in embodied intelligence. Giulia d’Angelo et al. contribute to progress in this field by developing a framework for benchmarking neuromorphic controllers on soft robotic platforms.

Identifying cell–cell interactions from imaging-based spatial transcriptomics suffers from limited gene panels. A new self-supervised graph transformer-based method can resolve spatial single-cell-level interactions without requiring known ligand–receptor pairs.

Mixtures are ubiquitous in industrial formulations. A framework unifying predictive and generative machine learning now offers a blueprint for data-driven design of multi-component battery electrolytes.

Capturing the complexity of cardiovascular dynamics demands multiple monitoring modalities, each with inherent trade-offs. Diffusion-based modeling offers a promising route for synthesizing and generating cross-modal data.