Nature Machine Intelligence, Published online: 05 August 2026; doi:10.1038/s42256-026-01275-z
A new deep learning method enables molecular dynamics simulations over longer time scales while still achieving accurate physical property prediction.Nature Machine Intelligence, Published online: 03 August 2026; doi:10.1038/s42256-026-01282-0
Generative machine learning methods have led to progress in crystal discovery, but cannot fully explore the space of material candidates that are both novel and useful. A reinforcement learning-based method steers candidate generation to these areas, enabling the design of novel functional materials.Nature Machine Intelligence, Published online: 03 August 2026; doi:10.1038/s42256-026-01278-w
Xiao et al. show that large language models partially align with human brain activity during deductive reasoning. They further show that brain signals can directly guide and improve model performance, with transfer across reasoning types.Nature Machine Intelligence, Published online: 30 July 2026; doi:10.1038/s42256-026-01284-y
Gao et al. introduce a quantum data augmentation method to enable neural networks to classify multipartite entanglement structures in infinite-dimensional systems, substantially improving accuracy and reducing the data acquisition costs that typically limit training.Nature Machine Intelligence, Published online: 30 July 2026; doi:10.1038/s42256-026-01277-x
Lai et al. extend and evaluate a unified framework for liquid electrolyte design, showing how data size and composition affect robustness, and demonstrating improved cross-system transferability and multiscale performance over baselines.Nature Machine Intelligence, Published online: 24 July 2026; doi:10.1038/s42256-026-01288-8
Training machine learning models on high-quality biological datasets can quickly produce abundant results. But as datasets often evolve over time, further work is required to update models and maintain alignment between models and datasets.Nature Machine Intelligence, Published online: 24 July 2026; doi:10.1038/s42256-026-01268-y
A controlled study of large language model agents across 260 configurations shows when multi-agent collaboration helps or hurts performance, and introduces a predictive model that selects the best architecture in 87% of held-out within-domain configurations.Nature Machine Intelligence, Published online: 21 July 2026; doi:10.1038/s42256-026-01254-4
Lin et al. introduce a brain-inspired generative model that provides two key features of intelligence: planning and problem-solving. It uses cognitive maps, stochastic computing and compositional coding, and requires only local synaptic plasticity.Nature Machine Intelligence, Published online: 20 July 2026; doi:10.1038/s42256-026-01274-0
Li et al. show that recurrent neural networks optimized for free recall discover diverse, human-like memory strategies beyond classical temporal context models, with top models using an index-based mechanism resembling the memory palace technique.Nature Machine Intelligence, Published online: 15 July 2026; doi:10.1038/s42256-026-01265-1
Moving beyond brute-force simulations, local neural operators—combined with equation-free methods and Krylov subspace techniques—enable system-level stability and bifurcation analysis of complex spatiotemporal systems directly from data.Nature Machine Intelligence, Published online: 14 July 2026; doi:10.1038/s42256-026-01271-3
Realigning AI technology towards the Sustainable Development GoalsNature Machine Intelligence, Published online: 14 July 2026; doi:10.1038/s42256-026-01259-z
Kindt et al. present a unifying framework for superposition in neural networks. Their three-step approach clarifies how latent features can be identified, disentangled and assessed.Nature Machine Intelligence, Published online: 13 July 2026; doi:10.1038/s42256-026-01273-1
The brain’s architecture exhibits diversity across many temporal and spatial scales, yet our computing architectures remain largely homogeneous. Low-powered neuromorphic hardware offers a path towards energy-efficient AI, but could these approaches be improved with heterogeneous computing architectures?Nature Machine Intelligence, Published online: 13 July 2026; doi:10.1038/s42256-026-01272-2
Sartore et al. present ergoCub, a humanoid robot that prioritizes human safety at hardware and motion levels. Using a shared embodied intelligence framework, design and control are jointly optimized with human-related metrics such as back stress alongside locomotion objectives, reducing spinal load and improving walking robustness.Nature Machine Intelligence, Published online: 13 July 2026; doi:10.1038/s42256-026-01260-6
Song et al. propose Sustainability Robotics as a new discipline to overcome fragmentation and enhance societal and environmental impact. They define three guiding principles, alongside two dimensions spanning sustainable design and robotics for sustainability.Nature Machine Intelligence, Published online: 10 July 2026; doi:10.1038/s42256-026-01270-4
The push to make biological data ‘AI-ready’ is accelerating worldwide. But to understand what AI-ready means for biological data, there are several questions to answer.Nature Machine Intelligence, Published online: 06 July 2026; doi:10.1038/s42256-026-01262-4
Park and Walsh introduce a reinforcement learning framework that could accelerate the discovery of new, thermodynamically stable and diverse crystalline materials with desired properties.Nature Machine Intelligence, Published online: 03 July 2026; doi:10.1038/s42256-026-01267-z
Berner et al. show how to adapt popular neural networks into discretization-agnostic neural operators that learn from continuous scientific data, enabling scientific simulations that generalize more reliably across resolutions.Nature Machine Intelligence, Published online: 02 July 2026; doi:10.1038/s42256-026-01266-0
Wang et al. develop DeepEvidence, a biomedical deep research agent for exploring and synthesizing evidence across various knowledge sources to support drug discovery, clinical trials and evidence-based medicine.Nature Machine Intelligence, Published online: 02 July 2026; doi:10.1038/s42256-026-01264-2
Liu and colleagues introduce HelixFold-S1, a guided sampling strategy for biomolecular complex structure prediction that targets high-probability interaction regions. The method achieves higher accuracy than traditional unguided methods while reducing computational costs.
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