Volume 6 Issue 6, June 2026

Although social technologies are increasingly co-shaping the public sphere, these systems were not designed as democratic infrastructure. Here, we propose a framework for societal alignment that focuses on procedural fairness to mitigate urgent risks and align emerging technology with societal values.

A gene–environment interaction test (SAGELD) is developed to speed up large-scale genome-wide analyses using longitudinal data, facilitating novel discoveries on genetic variants that modify the effects of environmental exposures.

Understanding how people move through cities is essential for public health and urban planning, yet most cities worldwide lack reliable mobility data. A new model reconstructs these movement patterns from openly available maps and population data, revealing that income inequality shapes urban mobility in ways that transcend local geography.

The Open Materials 2024 dataset provides a large-scale, open-access collection of quantum-chemical atomistic simulations that encompasses diverse off-equilibrium crystal structures, thereby making machine-learning interatomic potentials more robust across different materials prediction tasks.

A new library of combinatorial optimization problems provides a standardized testing ground to rigorously compare quantum and classical algorithms, paving the way for demonstrating quantum utility.

We developed FLOWR, a structure-aware multi-purpose generative model unifying de novo ligand generation, fragment-based and interaction-conditional sampling. We demonstrated that FLOWR generates ligands up to 70-fold faster compared with existing methods while improving the physical validity and interaction accuracy of generated compounds.

This Perspective explores the extent to which AI democratizes knowledge work and discusses potential intervention strategies for addressing social and place-based divides.

FLOWR combines continuous and categorical flow matching with equivariant optimal transport and a separate protein pocket encoder to improve validity, pose accuracy and interaction recovery at substantially faster inference speeds.

The authors introduce SAGELD, a method that leverages longitudinal data to detect gene–environment interactions with greater statistical power than conventional approaches based on cross-sectional data, improving discovery in large biobank studies.

This study introduces a unified framework for brain MRI tissue segmentation and region parcellation across the lifespan, demonstrating robust and consistent performance across heterogeneous datasets using a single model.

This work introduces an unsupervised method that restores high-quality Raman hyperspectral images from low-light measurements, enabling faster, lower-power imaging and expanding the use of Raman techniques in biological and chemical analysis.

PoTS is an automated pipeline that maps reaction transition states inside zeolite pores. By identifying hundreds of confined transition states across many frameworks, it explains differences in catalytic selectivity and informs zeolite design.

This study presents neuroGravity—a model that merges physics with machine learning to reconstruct mobility networks in data-scarce regions. It provides crucial tools for equitable urban planning, revealing income segregation as a core determinant of human flows.

This Resource introduces the Open Materials 2024 (OMat24) dataset, offering more than 110 million diverse density functional theory calculations. AI models trained on OMat24 achieve a high accuracy and can eliminate systematic biases across model architectures.

This Resource presents the Quantum Optimization Benchmarking Library, which enables fair, reproducible benchmarks of quantum heuristics for ten difficult combinatorial optimization classes with baseline results to track progress towards quantum advantage.