Results 141 to 150 of about 20,135,100 (276)

From Top to Bottom: Manufacturing Process‐Context Aware Resolution of Energy Device Electrodes Through a 3D Diffusion Generative Model

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, Siwar Ben Hadj Ali, Maxence Desnoyers, Francisco Fernandez, Nassima Benammar, Alejandro A. Franco   +5 more
wiley   +1 more source

Free Charge Generation in Organic Photoconversion Devices–the Impact of Electrostatic Potentials

Advanced Energy Materials, EarlyView.
Free charge generation in organic photovoltaic and photodetector devices typically requires an external driving force to separate bound excitons formed upon photoexcitation. This review details how electrostatic potentials, arising from molecular charge distributions in organic semiconductors, can impact interfacial energetic landscapes and promote ...
Emily J. Yang, Song Yi Park, Vick Y. Qiu, Ji‐Seon Kim   +3 more
wiley   +1 more source

Multiscale Prediction of Equivalent Elastic Properties of Carbon/Glass Hybrid Composite Laminates. [PDF]

Polymers (Basel)
Luo Q, Zhang L, Zhang F, Tuo T, Wu Y, Ding A.   +5 more
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

Multiscale Modeling and Analysis of Muscle Tissue: A Finite Element Approach for 3D Braided Composite Structures. [PDF]

Biomimetics (Basel)
Dhimole VK, Kumar N, Lee SY, Park SJ.
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

Integrating serial block-face SEM with voxel-based finite element analysis for high-fidelity micromechanical modelling of anisotropic soft tissues: application to human dermis. [PDF]

Biomech Model Mechanobiol
Li J, Katsamenis OL, Limbert G.
europepmc   +1 more source

Autonomous AI‐Driven Design for Skin Product Formulations

Advanced Intelligent Discovery, EarlyView.
This review presents a comprehensive closed‐loop framework for autonomous skin product formulation design. By integrating artificial intelligence‐driven experiment selection with automated multi‐tiered assays, the approach shifts development from trial‐and‐error to intelligent optimisation.
Yu Zhang, Yongrui Xiao, Chunlin Chen, Dimitrios Tsaoulidis, Kai Yang, Tao Chen   +5 more
wiley   +1 more source

Application of Local Approaches to Crack Development in Structural Materials. [PDF]

Materials (Basel)
Kozák V, Vala J.
europepmc   +1 more source

Multiscale and Multi‐Timestep Switching of Multiple Machine Learning Force Fields for Artificial Intelligence‐Driven Materials Simulations

Advanced Intelligent Discovery, EarlyView.
Deep Potential model switching accelerates molecular dynamics by using a faster 4 Å model for most timesteps and periodically applying a high‐accuracy 6 Å model. Validation on solid TiO2 and liquid PEG shows preserved RDF correlations and stable NPT behavior, while NVE energy‐drift analyses identify cases requiring additional validation.
Ryuya Kanda, Megumu Yamazaki, Yuta Yoshimoto, Shintaro Izumi, Atsuki Inoue, Hiroshi Kawaguchi, Yasufumi Sakai   +6 more
wiley   +1 more source