Machine Learning Enabled Screening of Single Atom Alloys: Predicting Reactivity Trend for Ethanol Dehydrogenation
Sign Up to like & getrecommendations! Published in 2021 at "ChemCatChem"
DOI: 10.1002/cctc.202101481
Abstract: A machine learning (ML) approach implementing the gradient boosting regressor (GBR) algorithm is applied to predict the binding energies of oxygen (EO) and carbon (EC) atoms on single atom alloys (SAAs) of Cu, Ag and… read more here.
Keywords: learning enabled; dehydrogenation; atom alloys; machine learning ... See more keywords
Deep Learning‐Enabled Automated Quality Control for Liver MR Elastography: Initial Results
Sign Up to like & getrecommendations! Published in 2024 at "Journal of Magnetic Resonance Imaging"
DOI: 10.1002/jmri.29490
Abstract: Several factors can impair image quality and reliability of liver magnetic resonance elastography (MRE), such as inadequate driver positioning, insufficient wave propagation and patient‐related factors. read more here.
Keywords: quality; deep learning; quality control; enabled automated ... See more keywords
Deep Learning-Enabled Unbiased Precision Toxicity Assessment of Zebrafish Organ Development.
Sign Up to like & getrecommendations! Published in 2025 at "Environmental science & technology"
DOI: 10.1021/acs.est.5c10763
Abstract: Precise assessment of toxicological effects remains a key bottleneck in biomedical and environmental health assessments. Traditional toxicology relies on macroscopic end points and manual image analysis, which limit sensitivity to structural damage and introduce subjective… read more here.
Keywords: deep learning; organ; assessment; toxicity assessment ... See more keywords
Machine Learning-Enabled Framework for High-Throughput Screening of MOFs: Application in Radon/Indoor Air Separation.
Sign Up to like & getrecommendations! Published in 2022 at "ACS applied materials & interfaces"
DOI: 10.1021/acsami.2c19207
Abstract: Radon and its progeny may cause severe health hazards, especially for people working in underground spaces. Therefore, in this study, a hybrid artificial intelligence machine learning-enabled framework is proposed for high-throughput screening of metal-organic frameworks… read more here.
Keywords: learning enabled; enabled framework; machine learning; framework ... See more keywords
A Machine Learning-Enabled SERS Sensor: Multiplex Detection of Lipopolysaccharides from Foodborne Pathogenic Bacteria.
Sign Up to like & getrecommendations! Published in 2025 at "ACS applied materials & interfaces"
DOI: 10.1021/acsami.5c08361
Abstract: Foodborne pathogenic bacteria pose a global threat to public health, causing dangerous and expensive outbreaks. While therapeutics are being developed to target antibiotic-resistant and foodborne pathogenic bacteria, approaches to minimize their spread are also crucial.… read more here.
Keywords: detection; foodborne pathogenic; machine learning; learning enabled ... See more keywords
Machine Learning-Enabled Design of Point Defects in 2D Materials for Quantum and Neuromorphic Information Processing.
Sign Up to like & getrecommendations! Published in 2020 at "ACS nano"
DOI: 10.1021/acsnano.0c05267
Abstract: Engineered point defects in two-dimensional (2D) materials offer an attractive platform for solid-state devices that exploit tailored opto-electronic, quantum emission, and resistive properties. Naturally occurring defects are also unavoidably important contributors to material properties and… read more here.
Keywords: point defects; machine learning; learning enabled; defects materials ... See more keywords
Deep-Learning-Enabled On-Demand Design of Chiral Metamaterials.
Sign Up to like & getrecommendations! Published in 2018 at "ACS nano"
DOI: 10.1021/acsnano.8b03569
Abstract: Deep-learning framework has significantly impelled the development of modern machine learning technology by continuously pushing the limit of traditional recognition and processing of images, speech, and videos. In the meantime, it starts to penetrate other… read more here.
Keywords: demand design; chiral metamaterials; deep learning; learning enabled ... See more keywords
Machine learning-enabled prediction of antimicrobial resistance in foodborne pathogens
Sign Up to like & getrecommendations! Published in 2024 at "CyTA - Journal of Food"
DOI: 10.1080/19476337.2024.2324024
Abstract: ABSTRACT The World Health Organization (WHO) has identified antimicrobial resistance (AMR) as one of the top three global dangers to public health. One of the most vital factors contributing to the high prevalence of AMR… read more here.
Keywords: antimicrobial resistance; foodborne pathogens; learning enabled; machine learning ... See more keywords
Graph deep learning enabled spatial domains identification for spatial transcriptomics.
Sign Up to like & getrecommendations! Published in 2023 at "Briefings in bioinformatics"
DOI: 10.1093/bib/bbad146
Abstract: Advancing spatially resolved transcriptomics (ST) technologies help biologists comprehensively understand organ function and tissue microenvironment. Accurate spatial domain identification is the foundation for delineating genome heterogeneity and cellular interaction. Motivated by this perspective, a graph… read more here.
Keywords: learning enabled; enabled spatial; deep learning; graph deep ... See more keywords
Barrier-Certified Learning-Enabled Safe Control Design for Systems Operating in Uncertain Environments
Sign Up to like & getrecommendations! Published in 2022 at "IEEE/CAA Journal of Automatica Sinica"
DOI: 10.1109/jas.2021.1004347
Abstract: This paper presents learning-enabled barrier-certified safe controllers for systems that operate in a shared environment for which multiple systems with uncertain dynamics and behaviors interact. That is, safety constraints are imposed by not only the… read more here.
Keywords: learning enabled; safety; barrier certified; control ... See more keywords
Federated-Learning-Enabled Cross-Modal Semantic Communication for 6G
Sign Up to like & getrecommendations! Published in 2025 at "IEEE Internet of Things Journal"
DOI: 10.1109/jiot.2025.3590596
Abstract: In view of super-large scale access and dynamic connectivity requirements in 6G, the number of users and data is increasing exponentially, which makes it difficult to achieve sustainable development of communications. Meanwhile, with the development… read more here.
Keywords: modal semantic; cross modal; federated learning; cross ... See more keywords