Results 211 to 220 of about 2,645,187 (293)

Entanglement Rényi negativity of interacting fermions from quantum Monte Carlo simulations. [PDF]

Nat Commun
Wang FH, Xu XY.
europepmc   +1 more source

Model predictive control with inline parameter adaptation for direct crystal growth rate regulation

AIChE Journal, EarlyView.
Abstract In batch cooling crystallization, many interactive factors, including supersaturation, reactor dimensions, and operating conditions, govern crystal growth and significantly influence product properties. Variables such as temperature or refractive index are used as surrogate control variables but have limitations in capturing growth rate ...
Huitian Yu, Jiewen Zhao, Heiko Briesen
wiley   +1 more source

Development of population pharmacokinetic models of total and free piperacillin in critically ill children and young adults for Monte Carlo simulations and model-informed precision dosing. [PDF]

J Antimicrob Chemother
Tang Girdwood S, Hambrick HR, Morales Junior R, Punt N, Tang P, Curry C, Lahni P, Jones R, Stoneman E, Gibson A, Kaplan J.   +10 more
europepmc   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

Monte Carlo Simulations of Polymer Collapse in an Explicit Solvent of Varying Quality. [PDF]

Polymers (Basel)
Polanowski P, Sikorski A.
europepmc   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

ZacrosTools: A Python Library for Automated Preparation, Analysis, and Visualization of Kinetic Monte Carlo Simulations with Zacros. [PDF]

J Phys Chem A
Prats H.
europepmc   +1 more source

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

Comparative analysis of Monte Carlo simulations and experimental evaluation of PMMA reinforced with hgo for gamma radiation shielding. [PDF]

Sci Rep
Mokhtari Dorostkar M, Abdi Saray A.
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