Results 201 to 210 of about 22,253 (300)
Machine Learning for Superconductor Discovery: From Data-Driven Insights to Accelerated Design. [PDF]
Zhang J +12 more
europepmc +1 more source
A donor–acceptor covalent organic framework is designed as an ambipolar cathode for aluminum‐ion energy storage. The crystalline, microporous architecture enables intrinsic charge transport without conductive additives. Multi‐electron redox activity at donor and acceptor sites supports high capacity, excellent stability, and efficient reversible ...
Cataldo Valentini +11 more
wiley +1 more source
In this study we employed support vector regressor and quantum support vector regressor to predict the hydrogen storage capacity of metal–organic frameworks using structural and physicochemical descriptors. This study presents a comparative analysis of classical support vector regression (SVR) and quantum support vector regression (QSVR) in predicting ...
Chandra Chowdhury
wiley +1 more source
Energy‐transfer‐mediated magnetoluminescence of paramagnetic transition metal complexes: dope solids of an octahedral MnII complex into the corresponding ZnII complex exhibited a pronounced magnetic field response of emission spectra under the application of magnetic fields.
Asato Mizuno +5 more
wiley +2 more sources
Impact of gap anisotropy of Polar and Anderson-Brinkman-Morel p-wave superconductors on thermoelectric properties of quantum dot hybrids. [PDF]
Sonar V, Trocha P.
europepmc +1 more source
A Critical Assessment of Bonding Descriptors for Predicting Materials Properties
The impact of new bonding descriptors in machine learning models for predicting material properties is assessed. Improvements are validated using significance tests, and new, intuitive descriptors for screening lattice thermal conductivity and projected force constants are introduced.
Aakash Ashok Naik +6 more
wiley +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
The path to room-temperature superconductivity: A programmatic approach. [PDF]
Prasankumar RP +15 more
europepmc +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
Reentrant superconductivity in a naturally occurring Josephson junction array tuned by radio-frequency power. [PDF]
Avraham S +6 more
europepmc +1 more source

