Results 71 to 80 of about 199,179 (229)
Machine Learning for Organic Fluorescent Materials
Organic fluorescent materials (OFMs) have demonstrated significant potential in diverse applications. Conventional approaches for studying OFMs face significant limitations in fluorescence spectroscopy and computational methods. Machine learning (ML) has revolutionized materials chemistry, offering superior predictive accuracy and efficiency over ...
Jiamin Zhong+7 more
wiley +1 more source
On minimizing the number of ADMs in a general topology optical network
M. Flammini, Mordechai Shalom, S. Zaks
semanticscholar +1 more source
In Situ Graph Reasoning and Knowledge Expansion Using Graph‐PRefLexOR
Graph‐PRefLexOR is a novel framework that enhances language models with in situ graph reasoning, symbolic abstraction, and recursive refinement. By integrating graph‐based representations into generative tasks, the approach enables interpretable, multistep reasoning.
Markus J. Buehler
wiley +1 more source
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
Deep Learning‐Assisted Design of Mechanical Metamaterials
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+5 more
wiley +1 more source
The article introduces WACEfNet, a new convolutional neural network architecture optimized for efficient aerial image analysis under resource constraints. It creatively integrates attention mechanisms and atrous convolutions into a compact widened residual network framework.
Md Meftahul Ferdaus+4 more
wiley +1 more source
Advancements in Machine Learning for Microrobotics in Biomedicine
Microrobotics is an innovative technology with great potential for noninvasive medical interventions. However, controlling and imaging microrobots pose significant challenges in complex environments and in living organisms. This review explores how machine learning algorithms can address these issues, offering solutions for adaptive motion control and ...
Amar Salehi+6 more
wiley +1 more source
Uncontrolled Learning: Codesign of Neuromorphic Hardware Topology for Neuromorphic Algorithms
Codesign is used to implement a neuroscience‐inspired machine learning algorithm in all neuromorphic hardware. In this implementation, the hidden memristors cannot be directly accessed, limiting control of the network during training. By leveraging theoretical tools, including memristor circuits dynamics and a closed form expression for the network ...
Frank Barrows+3 more
wiley +1 more source
Applied Artificial Intelligence in Materials Science and Material Design
AI‐driven methods are transforming materials science by accelerating material discovery, design, and analysis, leveraging large datasets to enhance predictive modeling and streamline experimental techniques. This review highlights advancements in AI applications across spectroscopy, microscopy, and molecular design, enabling efficient material ...
Emigdio Chávez‐Angel+7 more
wiley +1 more source
Mainstream Artificial Intelligence Technologies in Contemporary Ophthalmology
This review explores the latest artificial intelligence (AI) technologies in ophthalmology, focusing on four key data types: medical imaging, electronic health records, robotic‐assisted surgery, and genomics. It examines the structural features, use cases, clinical goals, and evaluation metrics of various AI algorithms, while also introducing emerging ...
Shiqi Yin+9 more
wiley +1 more source