Results 111 to 120 of about 8,215 (265)
This review offers a comprehensive comparison between perovskites and perovskite‐inspired materials (PIMs), focusing on their crystal structures, electronic properties, and chemical compositions. It evaluates the applicability of machine learning (ML) descriptors and models across both material classes.
Yangfan Zhang +6 more
wiley +1 more source
All Organic Fully Integrated Neuromorphic Crossbar Array
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 +2 more
wiley +1 more source
Terahertz Channel Modeling, Estimation and Localization in RIS‐Assisted Systems
Reconfigurable intelligent surfaces have become a recent intensive research focus. Based on practical applications, channel strategies for RIS‐assisted terahertz wireless communication systems are categorized into three different types: channel modeling, channel estimation, and channel localization.
Hongjing Wang +9 more
wiley +1 more source
While perovskite solar cells have been widely studied, including their stability, perovskite indoor photovoltaics (IPVs) have only recently emerged. Nevertheless, more studies are appearing in the literature. The systematic stability study of IPVs is crucial, particularly given the inconsistencies in reported methodologies and results, which call for ...
Ivy Mawusi Asuo +7 more
wiley +1 more source
Degradation Pathways of Silicon‐Based Anodes in Lithium‐Ion Batteries
Silicon‐based anodes undergo degradation through five primary pathways: (1) mechanical and structural deterioration of the active material, (2) loss of electrode integrity and electrical contact, (3) mechanical instability of the solid electrolyte interphase (SEI), characterized by repetitive fracture and deformation, (4) chemical instability of the ...
Yoon Jeong Choi +3 more
wiley +1 more source
Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories
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 +4 more
wiley +1 more source
Optimization of power management in PV-based smart grids using grid-support and grid-forming inverters. [PDF]
Bagdadee AH +4 more
europepmc +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
A low‐cost, self‐driving laboratory is developed to democratize autonomous materials discovery. Using this "frugal twin" hardware architecture with Bayesian optimization, the platform rapidly converges to target lower critical solution temperature (LCST) values while self‐correcting from off‐target experiments, demonstrating an accessible route to data‐
Guoyue Xu, Renzheng Zhang, Tengfei Luo
wiley +1 more source
Improving predictive reliability and automation of smart grids using the StarNet ensemble model. [PDF]
Chhabra A +10 more
europepmc +1 more source

