Results 191 to 200 of about 1,813,648 (289)
Ligand-Tuned CISS-Effect of Atomically Precise Metal Oxido Nanoclusters. [PDF]
Hornig D +6 more
europepmc +1 more source
Advancing Energy Materials by In Situ Atomic Scale Methods
Progress in in situ atomic scale methods leads to an improved understanding of new and advanced energy materials, where a local understanding of complex, inhomogeneous systems or interfaces down to the atomic scale and quantum level is required. Topics from photovoltaics, dissipation losses, phase transitions, and chemical energy conversion are ...
Christian Jooss +21 more
wiley +1 more source
A polarized high-field infrared instrument reveals giant infrared chirality in Weyl semimetals. [PDF]
Yu ZM, Yao Y.
europepmc +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
VibeTube: a code for accurate extraction of CNTs' chirality from TEM images. [PDF]
Tang D +5 more
europepmc +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
Chirality Transfer to the Magnetic Sublattice in the Hybrid Perovskite (R)-/(S)-3-Fluoropyrrolidinium Copper(II) Chloride. [PDF]
Zhang Z +6 more
europepmc +1 more source
Artificial intelligence is redefining network pharmacology (NP). By integrating knowledge graph engineering, geometric deep learning, multiomics anchoring, and generative reasoning, AI‐driven NP (AI‐NP) transforms static target mapping into dynamic, predictive modeling.
Cong Wang +9 more
wiley +1 more source
Chiral packings in cylinders are ultrasensitive to confinement deformation. [PDF]
Wang X, Guo J, Li Y.
europepmc +1 more source
Materials Representation Learning Based on a Material–Motif Network and Heterogeneous Graphs
Structure motifs in materials are used to construct a bipartite material–motif network that links each material to its constituent motifs and establishes connectivity among materials sharing common motifs. Network analysis reveals material clusters associated with different functional applications and supports motif‐guided screening of materials.
Anoj Aryal +3 more
wiley +1 more source

