Results 71 to 80 of about 122,876 (196)

All Organic Fully Integrated Neuromorphic Crossbar Array

Advanced Electronic Materials, EarlyView.
In this work, the first fully integrated crossbar array of electrochemical random‐access memory (ECRAM) that is composed entirely of organic materials is represented. This array can perform inference and in situ parallel training and is capable of classifying linearly separable 2D and 3D classification tasks with high accuracy.
Setareh Kazemzadeh, Tim Stevens, Yoeri van de Burgt   +2 more
wiley   +1 more source

Study of Resistive Switching Dynamics and Memory States Equilibria in Analog Filamentary Conductive‐Metal‐Oxide/HfOx ReRAM via Compact Modeling

Advanced Electronic Materials, EarlyView.
A physics‐based compact model for Conductive‐Metal‐Oxide/HfOx ReRAM, accounting for ion dynamics, electronic conduction, and thermal effects, is presented. Accurate and versatile simulations of analog non‐volatile conductance modulation and memory state stabilization enable reliable circuit‐level studies, advancing the optimization of neuromorphic and ...
Matteo Galetta, Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Stephan Menzel, Antonio La Porta, Tommaso Stecconi, Folkert Horst, Bert Jan Offrein, Valeria Bragaglia   +9 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

Animal‐Based Brands Taking the Plant‐Based Opportunity: A Tasting Experiment Exploring Consumer Acceptance of Plant‐Based Brand Extensions

Agribusiness, EarlyView.
ABSTRACT This study investigates how consumer taste and brand equity perceptions shape the acceptance of plant‐based milk products. Using a blind/informed tasting experiment, we evaluated consumers' willingness to buy (WTB) and taste perception of a plant‐based milk alternative produced by a traditional dairy brand, compared with competing plant‐based ...
Federico Parmiggiani, Mirta Casati, Elena Castellari, Fosca Vezzulli, Veronica Vitali, Milena Lambri, Claudio Soregaroli   +6 more
wiley   +1 more source

What to Make and How to Make It: Combining Machine Learning and Statistical Learning to Design New Materials

Advanced Intelligent Discovery, EarlyView.
Combining machine learning and probabilistic statistical learning is a powerful way to discover and design new materials. A variety of machine learning approaches can be used to identify promising candidates for target applications, and causal inference can help identify potential ways to make them a reality.
Jonathan Y. C. Ting, Amanda S. Barnard
wiley   +1 more source

Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers

Advanced Intelligent Discovery, EarlyView.
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia, Giovanna Pappalardo, Aldo R. Boccaccini, Liliana Liverani   +3 more
wiley   +1 more source

CrossMatAgent: AI‐Assisted Design of Manufacturable Metamaterial Patterns via Multi‐Agent Generative Framework

Advanced Intelligent Discovery, EarlyView.
CrossMatAgent is a multi‐agent framework that combines large language models and diffusion‐based generative AI to automate metamaterial design. By coordinating task‐specific agents—such as describer, architect, and builder—it transforms user‐provided image prompts into high‐fidelity, printable lattice patterns.
Jie Tian, Martin Taylor Sobczak, Dhanush Patil, Jixin Hou, Lin Pang, Arunachalam Ramanathan, Libin Yang, Xianyan Chen, Yuval Golan, Xiaoming Zhai, Hongyue Sun, Kenan Song, Xianqiao Wang   +12 more
wiley   +1 more source

Inverse Design of Alloys via Generative Algorithms: Optimization and Diffusion within Learned Latent Space

Advanced Intelligent Discovery, EarlyView.
This work presents a novel generative artificial intelligence (AI) framework for inverse alloy design through operations (optimization and diffusion) within learned compact latent space from variational autoencoder (VAE). The proposed work addresses challenges of limited data, nonuniqueness solutions, and high‐dimensional spaces.
Mohammad Abu‐Mualla, Ellis Crabtree, Fredrick Michael, Yayue Pan, Jida Huang   +4 more
wiley   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

Large Language Model in Materials Science: Roles, Challenges, and Strategic Outlook

Advanced Intelligent Discovery, EarlyView.
Large language models (LLMs) are reshaping materials science. Acting as Oracle, Surrogate, Quant, and Arbiter, they now extract knowledge, predict properties, gauge risk, and steer decisions within a traceable loop. Overcoming data heterogeneity, hallucinations, and poor interpretability demands domain‐adapted models, cross‐modal data standards, and ...
Jinglan Zhang, Xinyi Chen, Xu Ye, Yulin Yang, Bin Ai   +4 more
wiley   +1 more source