Results 161 to 170 of about 1,205,834 (315)

Why Physics Still Matters: Improving Machine Learning Prediction of Material Properties With Phonon‐Informed Datasets

Advanced Intelligent Discovery, EarlyView.
Phonons‐informed machine‐learning predictive models are propitious for reproducing thermal effects in computational materials science studies. Machine learning (ML) methods have become powerful tools for predicting material properties with near first‐principles accuracy and vastly reduced computational cost.
Pol Benítez, Cibrán López, Edgardo Saucedo, Teruyasu Mizoguchi, Claudio Cazorla   +4 more
wiley   +1 more source

Coupling Molecular and Cellular Dynamics in a Large-Scale Monte Carlo Simulation. [PDF]

Int J Mol Sci
Chaiken J, Ifrach A, Sajman J, Sherman E.   +3 more
europepmc   +1 more source

AI‐Guided Co‐Optimization of Advanced Field‐Effect Transistors: Bridging Material, Device, and Fabrication Design

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, Shauryaraj K. Jilkar, Aditya Udgaonkar, Yu (Joe) Lu, Ali Mesbah   +4 more
wiley   +1 more source

Feasibility of public CPR training kiosks to increase bystander resuscitation: a Monte Carlo simulation study. [PDF]

Resusc Plus
Ohle R, Savage DW, Roy D, Yadav K, Chhabra S, McIsaac S.   +5 more
europepmc   +1 more source

Parametric Analysis of Spiking Neurons in 16 nm Fin Field‐Effect Transistor Technology

Advanced Intelligent Discovery, EarlyView.
Energy efficient computing has driven a shift toward brain‐inspired neuromorphic hardware. This study explores the design of three distinct silicon neuron topologies implemented in 16 nm fin field‐Effect transistor technology. While the Axon‐Hillock design achieves gigahertz throughput, its functional fragility persists. The Morris–Lecar model captures
Logan Larsh, Raiyan Siddique, Sarah S. Sharif, Yaser Mike Banad   +3 more
wiley   +1 more source

Co-Administration Dosing of Ceftazidime-Avibactam and Aztreonam in Patients with Varying Renal Function for Carbapenem-Resistant Enterobacterales Infections via Monte Carlo Simulation. [PDF]

Infect Chemother
Hemapanpairoa J, Saelim W, Changpradub D, Santimaleeworagun W.   +3 more
europepmc   +1 more source

A review of the use and potential of the GATE Monte Carlo simulation code for radiation therapy and dosimetry applications.

Medical Physics (Lancaster), 2014
David Sarrut, M. Bardiès, N. Boussion, N. Freud, S. Jan, J. Létang, G. Loudos, L. Maigne, S. Marcatili, T. Mauxion, P. Papadimitroulas, Y. Perrot, U. Pietrzyk, C. Robert, D. Schaart, D. Visvikis, I. Buvat   +16 more
semanticscholar   +1 more source

Uncertainty‐Guided Selective Adaptation Enables Cross‐Platform Predictive Fluorescence Microscopy

Advanced Intelligent Discovery, EarlyView.
Deep learning models often fail when transferred to new microscopes. A novel framework overcomes this by selectively adapting the early layers governing low‐level image statistics, while freezing deep layers that encode morphology. This uncertainty‐guided approach enables robust, label‐free virtual staining across diverse systems, democratizing ...
Kai‐Wen K. Yang, Andrew Bai, Alexandra Bermudez, Yunqi Hong, Zoe Latham, Iris Sloan, Michael Liu, Vishrut Goyal, Cho‐Jui Hsieh, Neil Y. C. Lin   +9 more
wiley   +1 more source

Correction: Stochastic health risk profiling of potentially toxic elements in Iranian ornamental construction paints: assessing and Monte Carlo simulation. [PDF]

Sci Rep
Ataiesalami F, Tahergorabi M, Dorri H, Khodadadi M, Amiri MM, Rajabi S.   +5 more
europepmc   +1 more source

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

Advanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi, Kazuki Ishizaki, Ryo Fukasawa   +2 more
wiley   +1 more source