Results 41 to 50 of about 60,802 (187)
TarPass provides a rigorous benchmark for target‐aware de novo molecular generation by jointly evaluating protein‐ligand interactions, molecular plausibility, and drug‐likeness on 18 well‐studied targets. Results show that current models often fail to consistently surpass random baseline in target‐specific enrichment, while post hoc multi‐tier virtual ...
Rui Qin +11 more
wiley +1 more source
Disjointness of M\"{o}bius from asymptotically periodic functions
In this paper, we investigate asymptotically periodic functions from the point of view of operator algebra and dynamical systems. To study the M\"{o}bius disjointness of these functions, we prove a general result on the averages of multiplicative ...
Wei, Fei
core
Research Progress and Applications of Non‐Carrier‐Injection Electroluminescence
Non‐carrier‐injection electroluminescence (NCI‐EL) uses AC fields and displacement currents to trigger light from internal charge reservoirs, enabling minimalist emitters with remotely coupled terminals. This review maps shared mechanisms across organics, GaN, quantum dots, and TMDCs, compares planar, interdigital, single‐terminal, and coaxial designs,
Wei Huang +6 more
wiley +1 more source
Universal Oxychlorination Strategy in Halide Solid Electrolytes for All‐Solid‐State Batteries
A WO2Cl2‐driven oxychlorination strategy enables bulk oxygen incorporation into close‐packed LixMCl6 (M = Zr, Y, Er, In) halide lattices. Oxygen is selectively anchored by W6+ as lattice‐integrated [WO2Cl4]2− units, regulating the anionic framework, diversifying Li coordination, and weakening Li–Cl interactions.
Jae‐Seung Kim +13 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
Topology‐Aware Machine Learning for High‐Throughput Screening of MOFs in C8 Aromatic Separation
We screened 15,335 Computation‐Ready, Experimental Metal–Organic Frameworks (CoRE‐MOFs) using a topology‐aware machine learning (ML) model that integrates structural, chemical, pore‐size, and topological descriptors. Top‐performing MOFs exhibit aromatic‐enriched cavities and open metal sites that enable π–π and C–H···π interactions, serving as ...
Yu Li, Honglin Li, Jialu Li, Wan‐Lu Li
wiley +1 more source
Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley +1 more source
Harnessing Machine Learning to Understand and Design Disordered Solids
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source
Isolation and Reactivity of a Square‐Planar Trisamido Silane
We report the synthesis and comprehensive characterisation of a square‐planar Si(+IV) hydride supported by an unsymmetric, trianionic and dearomatised N,N,N‐pincer ligand. This system enables element–ligand cooperative reactivity as an alternative to silicon‐centred redox chemistry, illuminating a largely unexplored regime in high‐valent silicon ...
David M. J. Krengel +5 more
wiley +2 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

