AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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Low-level hierarchical discussion in HiMatGen provides an approach to materials design that sustains the exchange of scientific knowledge and computational evidence throughout iterative exploration as the agent population grows.
A neuro-symbolic framework in which LLM-derived embeddings encode riboswitch sequences, secondary structures, and ligand representations into a unified representation that captures riboswitch-ligand interaction context, while domain-informed rules encode explicit biochemical priors is presented.
CRISP is introduced, a large language model-assisted framework that treats representation construction as a rule-space exploration and compilation problem: it repeatedly samples target-relevant chemical rules without access to structures, labels or data splits, consolidates related concepts, and compiles each into an e...
This Perspective examines how recent advances in data‐driven modeling, high‐performance simulation, and autonomous experimentation are converging to accelerate the discovery of functional materials for next‐generation technologies—from energy storage and biomedicine to nanoelectronics and quantum devices.
This review examines emerging AI methodologies for accelerated materials discovery, with particular emphasis on how computational design, data infrastructure, synthesis planning, and autonomous experimentation can be connected into experimentally grounded workflows.
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