Results 171 to 180 of about 36,180 (215)
Harnessing Machine Learning to Understand and Design Disordered Solids
Advanced Intelligent Discovery, EarlyView.This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source
Nonlinear kinematic impacts on nanofluid flow across rough surface with numerical simulation. [PDF]
Sci RepKhan A +8 more
europepmc +1 more source
Advanced Intelligent Discovery, EarlyView.This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath +4 more
wiley +1 more source
Entropy generation and regression analysis of unsteady Carreau ternary hybrid nanofluid flow with electromagnetic and thermal influences. [PDF]
Discov NanoSindhu T, Jagadeeshkumar K.
europepmc +1 more source
Advanced Intelligent Discovery, EarlyView.The hydration behavior of C3S in seawater‐relevant solutions is studied based on experiments, boundary nucleation and growth (BNG) modeling, and machine learning. The main ions included in seawater modify hydration mechanisms, with MgCl2 showing the strongest acceleration effect at the same concentration.
Yanjie Sun +6 more
wiley +1 more source
Thermal Effects in Microfluidic Electrokinetic Flows: From Limitation to Design Opportunity. [PDF]
Micromachines (Basel)Roy T.
europepmc +1 more source
Predicting Performance of Hall Effect Ion Source Using Machine Learning
Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park +8 more
wiley +1 more source
Thermometric Based‐Microswimmers with Chemical and Optical Engines
Advanced Intelligent Systems, EarlyView.Temperature sensing at small scales is typically performed using passive luminescent particles. Here, an alternative approach is demonstrated by integrating upconversion thermometry into self‐propelled microswimmers powered by chemical fuels or light. This strategy offers a step toward dynamic thermal sensing at the microscale, relevant to both lab‐on ...
João M. Gonçalves, Katherine Villa
wiley +1 more source
Advanced Intelligent Systems, EarlyView.A volatile‐switching compact model of electrochemical metallization memory cells for neuromorphic architecture is developed and validated by reliable reproduction of device characterization measurements: I−V sweeps, SET kinetics, relaxation dynamics.
Rana Walied Ahmad +4 more
wiley +1 more source
Tailoring carbon shell thickness in graphene-Li<sub>2</sub>S-carbon nanocomposite cathodes for enhanced polysulfide control and electrochemical stability. [PDF]
RSC AdvAltalbawy FMA +9 more
europepmc +1 more source