Results 41 to 50 of about 360 (203)

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

A Physics Constrained Machine Learning Pipeline for Young's Modulus Prediction in Multimaterial Hyperelastic Cylinders Guided by Contact Mechanics

Advanced Intelligent Discovery, EarlyView.
A physics‐guided machine learning framework estimates Young's modulus in multilayered multimaterial hyperelastic cylinders using contact mechanics. A semiempirical stiffness law is embedded into a custom neural network, ensuring physically consistent predictions. Validation against experimental and numerical data on C.
Christoforos Rekatsinas, Panagiotis Krokidas, Vasileios Vavourakis, Clara Essmann, George Giannakopoulos   +4 more
wiley   +1 more source

Parametrization and Cartesian representation techniques for robust resolution of chemical equilibria [PDF]

International audienceChemical equilibria computations, especially those with vanishing species in the aqueous phase, lead to nonlinear systems that are difficult to solve due to gradient blow up.
Jonval, Maxime, Tran, Quang Huy, Ben Gharbia, Ibtihel, Cancès, Clément, Faney, Thibault   +4 more
core   +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

Two‐Dimensional Flow and Thermal Energy Transfer in Non‐Newtonian Casson–Carreau Ternary Hybrid Nanofluids Over Stationary and Moving Wedge Surfaces With Radiation and Magnetic Field Effects

Asia-Pacific Journal of Chemical Engineering, EarlyView.
ABSTRACT Efficient thermal management in advanced industrial systems requires improved heat transfer performance, particularly under non‐Newtonian fluid behavior and complex surface geometries. This study investigates the two‐dimensional flow and heat transfer characteristics of ternary hybrid nanofluids over both stationary and moving wedge surfaces ...
A. O. Akindele, A. M. Obalalu, M. O. Afolabi, Umair Khan, Samia Elattar   +4 more
wiley   +1 more source

Relaxed secant-type methods [PDF]

, 2018
We present a unified local and semilocal convergence analysis for secant-type methods in order to approximate a locally unique solution of a nonlinear equation in a Banach space setting.
Argyros, Ioannis K, Magreñán, Á. Alberto   +1 more
core  

Impact of Cattaneo–Christov Heat and Mass Flux on MHD Hybrid Nanofluid Flow Over an Extending Surface With Convective and Mass Flux Effects

Asia-Pacific Journal of Chemical Engineering, EarlyView.
ABSTRACT This study investigates the flow of a magnetized hybrid nanofluid over a permeable stretching surface. The mass and thermal transport within the system is regulated using the Cattaneo–Christov flux theory. The fluid is additionally subjected to thermophoresis, chemical reaction, Brownian motion, and activation energy effects.
Ebrahem A. Algehyne, Abdul Bariq, Mounirah Areshi, Fahad Maqbul Alamrani, Zehba Raizah, Izharul Haq, Anwar Saeed   +6 more
wiley   +1 more source

Radiative Hybrid Nanofluid Flow Over a Porous Riga Surface: A Fuzzy–ANN Modeling Approach

Asia-Pacific Journal of Chemical Engineering, EarlyView.
ABSTRACT This study proposes a fuzzy–ANN model to investigate the nonlinear thermal transport in a tangent hyperbolic (Tanh) hybrid nanofluid flow past a porous Riga surface, considering the effects of Rosseland diffusion, chemical reactions, and internal volumetric heating.
Azad Hussain, Rabia Zetoon, Reeha Iqbal
wiley   +1 more source

Semilocal convergence of a k-step iterative process and its application for solving a special kind of conservative problems [PDF]

, 2017
[EN] In this paper, we analyze the semilocal convergence of k-steps Newton's method with frozen first derivative in Banach spaces. The method reaches order of convergence k + 1.
Hernández-Verón, Miguel Angel, Teruel-Ferragud, Carles, Martínez Molada, Eulalia   +2 more
core   +1 more source

Humans are not unique: difficult birth is common in placental mammals

Biological Reviews, EarlyView.
ABSTRACT Human childbirth is widely presumed to be uniquely difficult and dangerous compared to birth in other mammals. Tight fetopelvic proportions can result in obstructed labour and contribute to high rates of maternal and neonatal mortality. Ideas summarised under the ‘obstetrical dilemma’ have contributed to this assumption by explaining difficult
Nicole D. S. Grunstra
wiley   +1 more source