Results 181 to 190 of about 114,035 (266)

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

Monte Carlo simulation for evaluating and optimizing the efficiency of BR and QIR maintenance strategies. [PDF]

Sci Rep
Cheikh K, Boudi EM, Rabi R, Mokhliss H.
europepmc   +1 more source

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

Health risk assessment of exposure to Polycyclic Aromatic Hydrocarbons in bread from Iranian markets: Application of monte carlo simulation approach. [PDF]

PLoS One
Khodadadi N, Saghi M, Mashayekhi S, Bashirian N, Asadi Touranlou F, Tavakoly Sany SB.   +5 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

The Learning Rate Is Not a Constant: Sandwich-Adjusted Markov Chain Monte Carlo Simulation. [PDF]

Entropy (Basel)
Vrugt JA, Diks CGH.
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

Comparing energy-integrating detector and photon-counting detector-based breast cone beam CTs for microcalcification detection via Monte Carlo simulation. [PDF]

Phys Med Biol
Ollah Ezzati A, Hu X, Qi M, Lai Y, Zhong Y, Yang K, Jia X.   +6 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

Metrological Evaluation of Metopimazine HPLC Assay: ISO-GUM and Monte Carlo Simulation Approaches. [PDF]

Pharmaceutics
Haidara H, Assirey EA, Saffaj T, Ihssane B.   +3 more
europepmc   +1 more source