Results 101 to 110 of about 9,526 (252)
A decoupled cycling architecture along with cost‐effective membrane and efficient catalyst is developed for asymmetric zinc‐air battery. The decoupled design ensures stable operation of catalyst and the pH‐dynamic influence on battery performance is explored, which provides paths for efficient utilization of pH‐decoupling electrolytes.
Yeshu Tan +6 more
wiley +2 more sources
In this work, the Doubao large language model (LLM) is involved in the formula derivation processes for Hubbard U determination regarding the second‐order perturbations of the chemical potential. The core ML tool is optimized for physical domain knowledge, which is not limited to parameter prediction but rather serves as an interactive physical theory ...
Mingzi Sun +8 more
wiley +1 more source
Glycine molecules induce a unique crystal packing into a supramolecular one‐dimensional columnar structure of octacyanidotungstates, which exhibits exceptionally strong antiferromagnetic superexchange interactions of J = −42.41(2) K between adjacent octacyanidotungstate units.
Tatsuya Konishi +8 more
wiley +2 more sources
Acoustic emissions from spin crossover complexes.
Listening to the acoustic noise emitted by molecular spin crossover materials reveals both reversible and irreversible microstructural phenomena associated with the spin transition, providing a simple tool to detect structural fatigability.
Kamel SM +8 more
europepmc +3 more sources
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
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi +2 more
wiley +1 more source
Revealing charge anisotropies in metal compounds via high-purity x-ray polarimetry
Linear polarization analysis of hard x-rays is employed to probe electronic anisotropies in metal-containing complexes with high selectivity. We use polarization-resolved nuclear forward scattering (PR-NFS) of synchrotron radiation at the 14.4 keV ...
Lena Scherthan +14 more
doaj +1 more source
ABSTRACT Metal‐CO2 batteries have recently emerged as an intriguing class of energy storage and conversion devices that simultaneously utilize and manage carbon dioxide. Originating from studies of CO2 contamination in metal‐air batteries, these systems have evolved into a distinct research direction, offering insights into CO2 electrochemistry and its
Sungmin Choi, Sooyeon Seok, Changmin Kim
wiley +1 more source
Long‐Range Interactions in Topological Superconducting Systems: A Mini Review
Long‐range interacting quantum systems are surveyed in this review, with an emphasis on the long‐range topological superconductor and its variants. Long‐range interactions decaying in a power‐law manner can lead to exotic phenomena that finds no analogue in short‐range regimes.
Juntong Ren, Haifeng Lü
wiley +1 more source

