Results 171 to 180 of about 43,364 (297)

Efficient classical sampling from Gaussian boson sampling distributions on unweighted graphs. [PDF]

Nat Commun
Zhang Y, Zhou S, Wang X, Wang Z, Yang Z, Yang R, Xue Y, Li T.   +7 more
europepmc   +1 more source

Automatic Determination of Quasicrystalline Patterns from Microscopy Images

Advanced Intelligent Discovery, EarlyView.
This work introduces a user‐friendly machine learning tool to automatically extract and visualize quasicrystalline tiling patterns from atomically resolved microscopy images. It uses feature clustering, nearest‐neighbor analysis, and support vector machines. The method is broadly applicable to various quasicrystalline systems and is released as part of
Tano Kim Kender, Marco Corrias, Cesare Franchini   +2 more
wiley   +1 more source

easytrack: A napari plugin for automated parameter tuning in cell tracking. [PDF]

MicroPubl Biol
Huygelen T, Lowe A, Mao Y, Vicente-Munuera P.   +3 more
europepmc   +1 more source

Artificial Intelligence‐Driven Insights into Electrospinning: Machine Learning Models to Predict Cotton‐Wool‐Like Structure of Electrospun Fibers

Advanced Intelligent Discovery, EarlyView.
Electrospinning allows the fabrication of fibrous 3D cotton‐wool‐like scaffolds for tissue engineering. Optimizing this process traditionally relies on trial‐and‐error approaches, and artificial intelligence (AI)‐based tools can support it, with the prediction of fiber properties. This work uses machine learning to classify and predict the structure of
Paolo D’Elia, Giovanna Pappalardo, Aldo R. Boccaccini, Liliana Liverani   +3 more
wiley   +1 more source

A robust multi-location evaluation of a machine learning framework for wind power forecasting. [PDF]

PLoS One
Ali U, Sufyan M, Ali S, Ahmad M, Rehman Khan SU, Raza N, Sultana J, Habib MA.   +7 more
europepmc   +1 more source

A Comprehensive Assessment and Benchmark Study of Large Atomistic Foundation Models for Phonons

Advanced Intelligent Discovery, EarlyView.
We benchmark six large atomistic foundation models on 2429 crystalline materials for phonon transport properties. The rapid development of universal machine learning potentials (uMLPs) has enabled efficient, accurate predictions of diverse material properties across broad chemical spaces.
Md Zaibul Anam, Ogheneyoma Aghoghovbia, Mohammed Al‐Fahdi, Lingyu Kong, Victor Fung, Ming Hu   +5 more
wiley   +1 more source

Musings from a Vectosaur: Malaria in 2026. [PDF]

Malariaworld J
Lluberas MF.
europepmc   +1 more source

PERFECT MATCHINGS AND

, 2008
1. Introduction A lattice in the plane divides the plane into elementary regions. A tile is the union oftwo elementary regions that share an edge. A region is a connected region in the plane whose boundary consists of lattice segments.
Perfect Powers
core  

Self‐Driving Laboratory Optimizes the Lower Critical Solution Temperature of Thermoresponsive Polymers

Advanced Intelligent Discovery, EarlyView.
A low‐cost, self‐driving laboratory is developed to democratize autonomous materials discovery. Using this "frugal twin" hardware architecture with Bayesian optimization, the platform rapidly converges to target lower critical solution temperature (LCST) values while self‐correcting from off‐target experiments, demonstrating an accessible route to data‐
Guoyue Xu, Renzheng Zhang, Tengfei Luo
wiley   +1 more source

Enumeration of Autocatalytic Subsystems in Large Chemical Reaction Networks. [PDF]

J Chem Theory Comput
Golnik R, Gatter T, Stadler PF, Vassena N.   +3 more
europepmc   +1 more source