Active Learning Accelerated Discovery of Stable Iridium Oxide Polymorphs for the Oxygen Evolution Reaction
Sign Up to like & getrecommendations! Published in 2020 at "Chemistry of Materials"
DOI: 10.1021/acs.chemmater.0c01894
Abstract: The discovery of high-performing and stable materials for sustainable energy applications is a pressing goal in catalysis and materials science. Understanding the relationship between a material’s ... read more here.
Keywords: active learning; iridium oxide; accelerated discovery; learning accelerated ... See more keywords
Deep Learning-Accelerated Designs of Tunable Magneto-Mechanical Metamaterials.
Sign Up to like & getrecommendations! Published in 2022 at "ACS applied materials & interfaces"
DOI: 10.1021/acsami.2c09052
Abstract: Metamaterials are artificially structured materials with unusual properties, such as negative Poisson's ratio, acoustic band gap, and energy absorption. However, metamaterials made of conventional materials lack tunability after fabrication. Thus, active metamaterials using magneto-mechanical actuation… read more here.
Keywords: metamaterials deep; design; learning accelerated; deep learning ... See more keywords
Active learning accelerated exploration of single-atom local environments in multimetallic systems for oxygen electrocatalysis
Sign Up to like & getrecommendations! Published in 2024 at "npj Computational Materials"
DOI: 10.1038/s41524-024-01432-1
Abstract: Single-atom catalysts (SACs) with multiple active sites exhibit high activity for a wide range of sluggish reactions, but identifying optimal multimetallic SAC is challenging due to the vast design space. Here, we present a self-driving… read more here.
Keywords: exploration single; accelerated exploration; learning accelerated; active learning ... See more keywords
Machine-learning accelerated prediction of two-dimensional conventional superconductors.
Sign Up to like & getrecommendations! Published in 2025 at "Materials horizons"
DOI: 10.1039/d4mh01753f
Abstract: We perform a large scale search for two-dimensional (2D) superconductors, by using electron-phonon calculations with density-functional perturbation theory combined with machine learning models. In total, we screened over 140 000 2D compounds from the Alexandria database.… read more here.
Keywords: accelerated prediction; machine; learning accelerated; machine learning ... See more keywords
Machine learning accelerated the prediction of mechanical properties of copper modified by TMDs based on molecular dynamics simulation
Sign Up to like & getrecommendations! Published in 2024 at "Physica Scripta"
DOI: 10.1088/1402-4896/ad69cf
Abstract: In this study, we constructed a dataset of elastic modulus and ultimate stress for copper material enhanced by Transition Metal Dichalcogenides (TMDs) through Molecular Dynamics (MD) simulations. Subsequently, leveraging chemical insights, we selected appropriate descriptors… read more here.
Keywords: material; machine; machine learning; learning accelerated ... See more keywords
Deep Learning-accelerated MRI in Body and Chest.
Sign Up to like & getrecommendations! Published in 2025 at "Journal of computer assisted tomography"
DOI: 10.1097/rct.0000000000001762
Abstract: Deep learning reconstruction (DLR) provides an elegant solution for MR acceleration while preserving image quality. This advancement is crucial for body imaging, which is frequently marred by the increased likelihood of motion-related artifacts. Multiple vendor-specific… read more here.
Keywords: deep learning; learning accelerated; accelerated mri; learning ... See more keywords
Machine learning accelerated prediction of Ce-based ternary compounds involving antagonistic pairs
Sign Up to like & getrecommendations! Published in 2024 at "Physical Review Materials"
DOI: 10.1103/physrevmaterials.9.053803
Abstract: The discovery of novel quantum materials within ternary phase spaces containing antagonistic pairs such as Fe with Bi, Pb, In, and Ag, presents significant challenges yet holds great potential. In this work, we investigate the… read more here.
Keywords: accelerated prediction; antagonistic pairs; ternary compounds; learning accelerated ... See more keywords
Learning-Accelerated ADMM for Distributed DC Optimal Power Flow
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Control Systems Letters"
DOI: 10.1109/lcsys.2020.3044839
Abstract: We propose a novel data-driven method to accelerate the convergence of Alternating Direction Method of Multipliers (ADMM) for solving distributed DC optimal power flow (DC-OPF) where lines are shared between independent network partitions. Using previous… read more here.
Keywords: learning accelerated; optimal power; distributed optimal; power flow ... See more keywords
From Antiretroviral to Antibacterial: Deep-Learning-Accelerated Repurposing and In Vitro Validation of Efavirenz Against Gram-Positive Bacteria
Sign Up to like & getrecommendations! Published in 2025 at "Molecules"
DOI: 10.3390/molecules30142925
Abstract: The repurposing potential of Efavirenz (EFV), a clinically established non-nucleoside reverse transcriptase inhibitor, was comprehensively evaluated for its in vitro antibacterial effect either alone or in combination with other antibacterial agents on several Gram-positive clinical… read more here.
Keywords: deep learning; antiretroviral antibacterial; learning accelerated; gram positive ... See more keywords