Results 201 to 210 of about 22,253 (300)

Machine Learning for Superconductor Discovery: From Data-Driven Insights to Accelerated Design. [PDF]

open access: yesACS Omega
Zhang J   +12 more
europepmc   +1 more source

Donor–Acceptor Covalent Organic Framework Enables Ambipolar Charge Storage in Aluminum‐Ion Energy Storage

open access: yesAdvanced Energy Materials, EarlyView.
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

Exploring Quantum Support Vector Regression for Predicting Hydrogen Storage Capacity of Nanoporous Materials

open access: yesAdvanced Intelligent Discovery, EarlyView.
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 an Octahedral MnII Complex Ligated With Bis(Diphenylphosphino)Methane Dioxide

open access: yesAngewandte Chemie, EarlyView.
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

A Critical Assessment of Bonding Descriptors for Predicting Materials Properties

open access: yesAdvanced Intelligent Discovery, EarlyView.
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

open access: yesAdvanced Intelligent Discovery, EarlyView.
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]

open access: yesProc Natl Acad Sci U S A
Prasankumar RP   +15 more
europepmc   +1 more source

From Data to Discovery: Machine Learning–Enabled Intelligent Characterization of Two‐Dimensional Materials

open access: yesAdvanced Intelligent Discovery, EarlyView.
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]

open access: yesNat Commun
Avraham S   +6 more
europepmc   +1 more source

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