Results 81 to 90 of about 555 (189)

Automated Extraction of Multicomponent Alloy Data Using Large Language Models for Sustainable Design

Advanced Science, EarlyView.
A large language model (LLM) based pipeline is developed to automatically extract a comprehensive and accurate multicomponent alloy database from literature corpus. The extracted dataset is integrated with sustainability indicators to identify potential alloys that outperform existing industrial benchmark materials in terms of both performance and ...
Aravindan Kamatchi Sundaram, Mohit Chakraborty, Sai Mani Kumar Devathi, B. Pabitramohan Prusty, Rohit Batra   +4 more
wiley   +1 more source

On one-way cellular automata with a fixed number of cells

, 2003
We investigate a restricted one-way cellular automaton (OCA) model where the number of cells is bounded by a constant number k, so-called kC-OCAs. In contrast to the general model, the generative capacity of the restricted model is reduced to the set of ...
Malcher, Andreas
core  

De Novo Design of Membrane‐Targeting Antimicrobial Peptides Against Gram‐Negative Bacteria Using a Generative Artificial Intelligence Framework

Advanced Science, EarlyView.
Antimicrobial resistance caused by Gram‐negative bacteria remains difficult to overcome due to the protective outer membrane. To address this challenge, a multi‐condition constrained generative AI framework, GenMTAMP is proposed for de novo membrane‐targeting antimicrobial peptide design by integrating physicochemical and spatial structure descriptors.
Jingxiao Yu, Da‐Wen Sun, QingYi Wei, Yibing Zhang, Jidong Tang, Hongbin Pu   +5 more
wiley   +1 more source

AI‐Physics‐Experiment Trinity for Integrated Protein Dynamics Modeling

Advanced Science, EarlyView.
This review unites experiments, physics‐based simulations, and AI as a synergistic triad for protein dynamics modeling. It highlights integrative strategies, resolves sampling and forcefield bottlenecks, and outlines challenges and future directions for accurate, interpretable conformational ensemble prediction.
Chen Shi, Minying Low, Peng Xiu, Kresten Lindorff‐Larsen, Yong Wang   +4 more
wiley   +1 more source

Spatiotemporal Targeting Randle Cycle and Immune Checkpoint for Potent Antitumor Therapy

Advanced Science, EarlyView.
A glucose oxidase‐based nanogel (GOX‐NG) system using catechol‐functionalized alginate, exhibits enhanced tumor penetration, prolonged retention, and sustained glucose depletion in the tumor microenvironment. When combined with a fatty acid oxidation inhibitor, it implements dual metabolic suppression, thereby enhancing ROS‐induced immunogenic cell ...
Yuan Gao, Zijian Gong, Yixuan Fu, Jianan Zheng, Xiao Sang, Binglin Chen, Qinzhi Su, Weiping Gao, Fei Duan, Jinqi Wei, Xuliang Deng, Xinyu Liu   +11 more
wiley   +1 more source

Modeling filtering predicates composition with Finite State Automata [PDF]

, 2011
Luigi Ciminiera, Ciminiera, Luigi, Risso, Fulvio Giovanni Ottavio, Marco Leogrande, Fulvio Risso, Leogrande, Marco   +5 more
core   +1 more source

Conformal Reconfigurable Intelligent Surfaces: A Cylindrical Geometry Perspective

Advanced Electronic Materials, EarlyView.
Cylindrical reconfigurable intelligent surfaces are explored for low‐complexity beam steering using one‐bit meta‐atoms. A multi‐level modeling approach, including optimization‐based synthesis, demonstrates that even minimal hardware can support directive scattering.
Filippo Pepe, Ivan Iudice, Giuseppe Castaldi, Marco Di Renzo, Vincenzo Galdi   +4 more
wiley   +1 more source

Emerging Memory and Device Technologies for Hardware‐Accelerated Model Training and Inference

Advanced Electronic Materials, EarlyView.
This review investigates the suitability of various emerging memory technologies as compute‐in‐memory hardware for artificial intelligence (AI) applications. Distinct requirements for training‐ and inference‐centric computing are discussed, spanning device physics, materials, and system integration.
Yoonho Cho, Dae‐won Kim, Yujin Kim, Jung‐Heum Na, Yuwon Jeong, Dong‐Yeop Lee, Shinhyun Choi   +6 more
wiley   +1 more source

Recurrent neural networks can learn simple, approximate regular languages

, 1999
A number of researchers have shown that discrete-time recurrent neural networks (DTRNN) are capable of inferring deterministic finite automata from sets of example and counterexample strings; however, discrete algorithmic methods are much better at this ...
Forcada Mikel L., Corbi-Bellot Antonio M., Maggini Marco, Gori Marco   +3 more
core   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

Advanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen, Zheyu Zhang, Kelvian T. Mularso, Bonghyun Jo, Hyun Suk Jung   +4 more
wiley   +1 more source