Results 201 to 210 of about 2,302,430 (290)

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

open access: yesAdvanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen   +4 more
wiley   +1 more source

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

open access: yesAdvanced 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   +19 more
wiley   +1 more source

A Large Scale Multi‐Modal Workflow for Battery Characterization: From Concept to Implementation

open access: yesAdvanced Energy Materials, EarlyView.
Isolated characterization techniques produce independent datasets and single‐property insights. However, progressively more holistic interpretations of battery‐material behavior is needed in the future. Here we demonstrate a coordinated multimodal workflow enabling the correlation of heterogeneous datasets and the construction of multidimensional ...
François Cadiou   +34 more
wiley   +1 more source

Design Principles of Electrocatalysts for Industrial‐Scale Water Electrolysis

open access: yesAdvanced Energy Materials, EarlyView.
This Review distills electrocatalyst design for industrial‐scale water electrolysis into integrated principles linking active‐site engineering, phase and lattice modulation, interfacial microenvironment regulation and mass‐transport control. By connecting catalyst families with HER/OER mechanisms and practical operating demands, it outlines pathways ...
Hong Tang, Ce Cui, John Wang
wiley   +1 more source

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

open access: yesAdvanced 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

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