Artificial Intelligence Medical Compendium

Explore the latest research on artificial intelligence and machine learning in medicine.

Showing 791 to 800 of 213,137 articles

Pattern-Aware Intelligence Enables Nondestructive, Rapid Quantification of High-Aspect-Ratio Silicon Etching.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
High-aspect-ratio (HAR) structures are widely used in microelectromechanical systems (MEMS), electronic devices, and advanced packaging. Accurate characterization of post-etch microstructural morphology is essential for process control and device rel... read more 

Machine Learning-Guided Surface Strain Engineering in Connected Platinum-Nickel Nanoparticle Catalysts for Advanced Oxygen Reduction Performance.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Engineering the surface structure of catalysts is critical for achieving high intrinsic activity in the oxygen reduction reaction (ORR). We report a machine-learning (ML)-guided materials design strategy for the synthesis of support-free, connected n... read more 

Hexagonal Boron Nitride on Liquid and Single-Crystal Copper: Operando X-Ray and Atomistic Insights into Growth and Interfacial Structure.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Two-dimensional (2D) hexagonal boron nitride (hBN) is a key dielectric for van der Waals nanoelectronics, however, its controlled synthesis by chemical vapor deposition remains challenging and poorly understood. In this context, the growth of hBN on ... read more 

Pharmacological advances in Candida auris: emerging antifungal mechanisms and next-generation therapeutic strategies.

Journal of enzyme inhibition and medicinal chemistry
Candida auris is a major public health concern worldwide due to its efficient transmission, environmental persistence, and broad resistance to approved antifungal classes. This review consolidates recent pharmacological developments in this regard, f... read more 

Multimodal approach to situation awareness classification using physiological sensors.

Ergonomics
Accurately predicting operators' situation awareness (SA) is important in many work contexts. However, current well-validated SA measurement methods require task interruption, motivating alternative measurement approaches. We developed a multimodal e... read more 

Interface Defects and Recombination in HTL-Free and Bilayer Cs2SnI6 Perovskite Solar Cells: Numerical Modeling and Machine Learning Analysis.

Langmuir : the ACS journal of surfaces and colloids
Perovskite solar cells (PSCs) are a promising technology for sustainable energy generation. Among lead-free perovskites, Cs2SnI6 offers high structural stability and environmental compatibility, making it ideal for next-generation PSCs. This work sys... read more 

Mechanistic insights and rational design of emerging iron-based environmental catalysts for water remediation via multiscale simulations.

Nanoscale
Emerging iron-based catalysts have attracted increasing attention in water remediation, yet fully elucidating their underlying catalytic mechanisms remains challenging. Multiscale simulations have consequently become essential theoretical tools. Here... read more 

DDCCNet: Physics-Enhanced Multitask Neural Networks for Data-Driven Coupled-Cluster.

Journal of chemical theory and computation
We present the data-driven coupled-cluster deep network (DDCCNet), a family of multitask, physics-enhanced deep learning architectures designed to predict coupled-cluster singles and doubles (CCSD) amplitudes and correlation energies from lower-level... read more 

A Lightweight Universal Machine-Learning Interatomic Potential via Knowledge Distillation for Scalable Atomistic Simulations.

Journal of chemical information and modeling
We introduce a lightweight universal machine-learning interatomic potential (uMLIP), SevenNet-Nano, based on the graph neural network architecture SevenNet and enabled by a knowledge-distillation framework. The model inherits the broad generalization... read more