Results 171 to 180 of about 2,009,217 (292)

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

A novel automated framework for multi-engine Monte Carlo model commissioning in proton therapy. [PDF]

Phys Imaging Radiat Oncol
Pi Y, Wang H, Zhang Y, Peng Z, Zeng X, Guo Y, Liu C.   +6 more
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

Probabilistic dietary exposure assessment of polycyclic aromatic hydrocarbons (PAHs) and its associated disease burden in Singapore. [PDF]

Sci Rep
Li A, Chen ME, Lim GS, Er B, Liu MH, Sin V, Seow WJ, Ong GY, Ponampalam R, Tan BKK, Khoo J, Chan JSH, Aung KT.   +12 more
europepmc   +1 more source

Factorization Machine with Iterative Quantum Reverse Annealing: A Python Package for Batch Black‐Box Optimization With Reverse Quantum Annealing

Advanced Intelligent Discovery, EarlyView.
Factorization machine with iterative quantum reverse annealing (FMIRA) leverages quantum reverse annealing to perform batch black‐box optimization. Factorization machine with quantum annealing (FMQA) is a widely used python package for solving black‐box optimization problems using D‐Wave quantum annealers.
Andrejs Tučs, Ryo Tamura, Koji Tsuda
wiley   +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

Analysis of Regulatory Inhalation Unit Risk Estimates for Cobalt: Comparison to Epidemiological Data. [PDF]

Risk Anal
Marshall LP, Viegas V, Maier A, Marsh G, Kreider ML.   +4 more
europepmc   +1 more source

Being, Nature, and Life in Aristotle: Essays in Honor of Alan Gotthelf [PDF]

, 2011
Johnson, Monte
core  

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

Learning to simulate realistic human diffuse reflectance spectra. [PDF]

J Biomed Opt
Hübner M, Bin Qasim A, Studier-Fischer A, Rees M, Tran Ba V, Nölke JH, Seidlitz S, Sellner J, Heinecke J, Brandt J, Özdemir B, Dreher K, Seitel A, Nickel F, Max Haney C, Kowalewski KF, Ayala L, Maier-Hein L.   +17 more
europepmc   +1 more source