Results 211 to 220 of about 11,486,084 (339)
This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath +4 more
wiley +1 more source
Object-centric diffusion policies for real-world robotic-arm imitation learning. [PDF]
Kasu PR, Um D.
europepmc +1 more source
Autonomous AI‐Driven Design for Skin Product Formulations
This review presents a comprehensive closed‐loop framework for autonomous skin product formulation design. By integrating artificial intelligence‐driven experiment selection with automated multi‐tiered assays, the approach shifts development from trial‐and‐error to intelligent optimisation.
Yu Zhang +5 more
wiley +1 more source
Modeling and optimizing trust-mediated influence in temporal heterogeneous networks. [PDF]
Chen F.
europepmc +1 more source
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi +2 more
wiley +1 more source
Entropy Regularization in Deep Reinforcement Learning: A Structured Review Across Classical Control, Generative Policies, and Reasoning Language Models. [PDF]
Taricco G.
europepmc +1 more source
Artificial Intelligence for Advanced Functional Materials: Progress and Emerging Frontiers
Artificial intelligence is transforming the discovery of functional materials by linking synthesis, characterization, simulation, and design in unified workflows. Advances in machine learning, autonomous experimentation, and foundation models are accelerating innovation across energy, electronics, and biomedicine, while revealing new frontiers for ...
Cristiano Malica +38 more
wiley +1 more source
From LLM narratives to parameterized cooperation policies in multi-agent systems. [PDF]
de Curtò J +4 more
europepmc +1 more source
Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy
This review systematically details how machine learning transforms X‐ray absorption spectroscopy (XAS) analysis. It covers advanced deep learning architectures for structure‐spectra mapping and inverse tasks, while discussing key challenges like the simulation‐to‐reality gap.
Melaku Lake Tegegne +5 more
wiley +1 more source
Policy snapshots cannot establish journal AI-policy failure. [PDF]
Wang Y.
europepmc +1 more source

