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
Optimized weight spectrum method for interference signal separation in FSO communication. [PDF]
Duan Z, Qiang S, Zhao H, Wang Y.
europepmc +1 more source
Artificial Intelligence for Advanced Functional Materials: Progress and Emerging Frontiers
Artificial intelligence is transforming the discovery of functional materials by linking synthesis, characterization, simulation, and design in unified workflows. Advances in machine learning, autonomous experimentation, and foundation models are accelerating innovation across energy, electronics, and biomedicine, while revealing new frontiers for ...
Cristiano Malica +38 more
wiley +1 more source
Evaluation of the effect of novel all-in-one irrigating solutions with and without diode laser activation on the radicular dentine composition: An <i>in vitro</i> attenuated total reflectance Fourier-transform infrared spectroscopy study. [PDF]
Halkai R, Halkai KR, Shaikh YAA.
europepmc +1 more source
This study introduces Cellular Material Network (CM‐Net), a pioneering machine learning architecture integrating physical information, to predict the mechanical properties of cellular materials. Comprehensive validation through simulations and experiments demonstrates its accuracy in predicting nonlinear behaviors, including initial peak compression ...
Sicong Zhou +5 more
wiley +1 more source
Measurement of atomic scattering factors by cryoelectron microscopy. [PDF]
Shtyrov A +11 more
europepmc +1 more source
Accelerating Materials Discovery: A Review of Machine Learning in X‐Ray Absorption Spectroscopy
This review systematically details how machine learning transforms X‐ray absorption spectroscopy (XAS) analysis. It covers advanced deep learning architectures for structure‐spectra mapping and inverse tasks, while discussing key challenges like the simulation‐to‐reality gap.
Melaku Lake Tegegne +5 more
wiley +1 more source
Spatial structure based deep feature fusion network for autism spectrum disorder classification. [PDF]
Shwetha KS +4 more
europepmc +1 more source
Piezoelectric Nanocomposite Hydrogel for Wireless Neural Stimulation and Tissue Augmentation
A cell/tissue supporting piezoelectric hydrogel system (PZ‐gel) that incorporates ferroelectric, pyroelectric, and piezoelectric ceramic material barium titanate into an alginate/carboxymethyl chitosan hydrogel. The PZ‐gel can be sonoactivated for wireless neural cell and tissue stimulation, with the potential to be used as a stimulatory cell substrate
Mohammad Mohammadi +3 more
wiley +1 more source
A hybrid AI framework for identification of power quality disturbances in electrical network. [PDF]
Debnath R, Majumder A, Jain AK, Dey B.
europepmc +1 more source

