Author

Alejandro A. Franco

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Open access Aug 2026

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

Optimization of electrochemical energy storage and conversion devices often depends on the electrode microstructure properties, of which its 3D representation provides the most complete link between manufacturing process and device performance. Deep generative models can address slow experimental throughput and high...

Victor Ramirez‐Camacho, Siwar Ben Hadj Ali, Maxence Desnoyers et al. · Advanced Energy Materials · 0 citations

Open access Jul 2026

AI-Driven Surrogate Models for Predicting Electrode-Scale Discharge Behavior in Lithium-Ion Batteries

Physics-based simulations are essential for understanding the electrode-scale discharge behavior of lithium-ion batteries (LIBs) but suffer from prohibitive computational costs. To address this, we introduce a novel deep learning surrogate pipeline based on the Swin3D Transformer to predict spatiotemporal discharge dyn...

Mengda Xing, Jean-Marie Lagniez, Alejandro A. Franco · International Conference on... · 0 citations

Review Jul 2026

(Invited) Physics-Informed Deep Learning for the 3D-Resolved Optimization of Lithium-Ion Battery Manufacturing Processes

This lecture reviews the recent progress within the ARTISTIC research initiative regarding the development of Physics-Informed Deep Learning frameworks to unravel the complex links between manufacturing parameters and battery electrode properties, and demonstrates how these fast surrogate models are coupled with macros...

Alejandro A. Franco · ECS Meeting Abstracts · 0 citations