Results 191 to 200 of about 16,392 (251)
Advanced Energy Materials, EarlyView.The application of a generative diffusion model, enhanced with a training data augmentation pipeline retaining the manufacturing process context of electrode microstructures, leads to improved fidelity of the through‐plane tortuosity factor in the AI generated samples.
Victor Ramirez‐Camacho +5 more
wiley +1 more source
Computational Theory of Mathematical Universe (CTMU)
The Computational Theory of Mathematical Universe (CTMU) proposes that the mathematical universe possesses an intrinsic computational structure. This theory posits that a fundamental rule, combined with an initial state, generates mathematical entities.
openaire +1 more source
Advanced Energy Materials, EarlyView.A novel operando methodology, which combines simultaneous rheological and electrochemical measurements under dc polarization in a LiTFSI‐doped polystyrene‐block‐poly(ethylene oxide) block copolymer electrolyte, is developed. Time‐resolved monitoring of the viscoelastic and electrochemical responses to galvanostatic stimulus enables to elucidate the ...
Martino Airoldi +2 more
wiley +1 more source
The Geography of Success: A Spatial Analysis of Export Intensity in the Italian Wine Industry
Agribusiness, EarlyView.ABSTRACT This paper investigates the paradox of how Italy's fragmented, SME‐dominated wine industry achieves global export success. Moving beyond purely firm‐centric explanations, we test whether export intensity is spatially dependent, clustering geographically in regional ecosystems.
Nicolas Depetris Chauvin, Jonas Di Vita
wiley +1 more source
AIChE Journal, EarlyView.Abstract Sorption in glassy polymer membranes is commonly modeled with the dual‐mode sorption (DMS) model. Fitting the DMS model to sorption isotherms presents challenges, as multiple parameter sets may prove satisfactory. This work presents pyDMS, an open‐source Python package for the computation of DMS parameters obtained via a physics‐informed ...
Brandon C. Tapia +4 more
wiley +1 more source
AIChE Journal, EarlyView.ABSTRACT Conceptual process design combines discrete configuration choices with continuous operating decisions, often yielding difficult mixed‐integer nonlinear or simulation‐based optimization problems. This work presents an exploratory computational assessment of Ising‐based solvers, simulated annealing, quantum annealing, and entropy computing, as ...
Yirang Park, David E. Bernal Neira
wiley +1 more source
Machine learning driven many‐objective moving horizon scheduling optimization
AIChE Journal, EarlyView.Abstract Industrial electrification can decarbonize chemical manufacturing, but it exposes operations to volatile electricity prices and carbon intensities. This work develops a machine learning‐enhanced many‐objective moving horizon scheduling framework that predicts objective correlation groupings from 48‐hour price and emission‐intensity profiles ...
Hongxuan Wang, Andrew Allman
wiley +1 more source
AIChE Journal, EarlyView.Abstract Despite the growing use of ML in chemical engineering, the catalytic conversion of sulfur dioxide (SO2) to sulfur trioxide (SO3) remains underexplored from a data‐driven modeling perspective. This study evaluates an integrated workflow for literature‐derived SO2 oxidation data, combining data curation, preprocessing assessment, machine ...
Farough Agin +2 more
wiley +1 more source
Experimental and numerical study of liquid–liquid systems using the Euler–Lagrange/VOF approach
AIChE Journal, EarlyView.Abstract Achieving stable liquid–liquid dispersions with controlled droplet size remains a challenge due to the complex interplay of dispersed phase viscosity, hydrodynamic conditions and interfacial interactions. This work presents a validated computational framework for droplet breakage and coalescence in liquid–liquid dispersions with variable ...
Pavel Krýsa +3 more
wiley +1 more source
Embedded Morgan Fingerprints for More Efficient Molecular Property Predictions With Machine Learning
Advanced Intelligent Discovery, EarlyView.High dimensionality of Morgan fingerprints causes overfitting and slow training. Embedded Morgan Fingerprint (eMFP) compresses this encoding while keeping key structural information intact. Tested on five regression models across three databases for HOMO–LUMO gap prediction, eMFP outperforms standard fingerprints, training faster on large, high ...
Emilio Nuñez‐Andrade +4 more
wiley +1 more source