Results 91 to 100 of about 7,179 (208)
Environmental Effects on RRAM Cells Based on 2D Halide Perovskite Materials
RRAM offers high speed, scalability, and low power, positioning it as a next‐generation non‐volatile memory. Two‐dimensional halide perovskites show promise due to tunable optoelectronic properties and flexible processing but suffer from environmental sensitivity. This review examines degradation from humidity, temperature, light, and strain, discusses
Mojtaba Joodaki +3 more
wiley +1 more source
Artificial Intelligence for Fluorite Ferroelectric Materials: From Discovery to Optimization
Artificial intelligence accelerates the discovery and optimization of HfO2‐based fluorite ferroelectrics by linking synthesis, structure, properties, and device performance. Machine learning, deep‐learning analysis, and AI‐driven atomistic modeling enable predictive design, dopant screening, and closed‐loop optimization toward next‐generation ...
Faizan Ali +3 more
wiley +1 more source
+4 more sources
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
Machine learning serves as a central engine for the intelligent characterization of two‐dimensional materials by integrating multimodal techniques, including optical microscopy, spectroscopy, electron microscopy, and scanning probe microscopy (SPM). This unified framework enables automated, high‐throughput, and quantitative extraction of structural ...
Zhi‐Long Cao, Jia‐Xu Yan
wiley +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
+4 more sources
On‐Surface Friedel–Crafts Alkylation Through a Stabilized Intermediate Complex
On‐surface Friedel–Crafts alkylation is demonstrated on Ag(111) at the single‐molecule level. STM, XPS, and DFT calculations uncover the reaction mechanism and explain the stabilization of the key intermediate, providing insight into noble‐metal‐surface‐mediated Csp3${\rm C}_{sp^{3}}$─Csp2${\rm C}_{sp^{2}}$ coupling. ABSTRACT The incorporation of sp3${\
Paul Schweer +5 more
wiley +1 more source
Self‐Assembled Telecom Color Centers in Silicon and Their Growth Environment
Influence of the growth pressure during the formation of self‐assembled color centers in silicon grown by molecular beam epitaxy at ultra‐low temperatures on the color center's photoluminescence spectral signature. The growth pressure crucially determines color center selectivity, as evidenced by G‐centers, W‐centers, and T‐centers, matrix purity, and ...
Jacqueline Marböck +9 more
wiley +1 more source
On the Design of a Class of Analog Filters Whose Impulse Response Is a Mittag‐Leffler Function
Fractional‐order filters whose impulse response is a Mittag‐Leffler (M‐L) function in any of its known forms are summarized in this work. Simulation results using Cadence and experimental results using an FPAA validate the introduced concept. ABSTRACT Fractional‐order filters whose impulse response is a Mittag‐Leffler (M‐L) function in any of its known
Julia Nako +3 more
wiley +1 more source

