Skip to content
Log in
Sponsored AI AM CLAUDE, A Beginner's Guide: Learn the basics of working with AI AI AM CLAUDE, A Beginner's Guide: Learn the basics of working with AI

Author

Alejandro A. Franco

h-index 8 View on Semantic Scholar

We have 5 of 45 papers

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Open access Oct 2026

Accelerated Parameter Optimization of Granular Models of Battery Manufacturing

Mesoscale particle simulations play a crucial role in investigating the manufacturing processes of electrochemical energy devices. They enable physical insights into the processes while building datasets to train AI surrogates for electrode microstructure and manufacturing optimization. Hence, the automation of paramet...

Sourab Barath Vijayaraghavan, Soorya Saravanan, M. Alabdali et al. · 0 citations
Open access Aug 2026

Two-Stage Chemical Degradation of PTFE with H- and HO-Radicals: A DFT Study Under PEMFC Operating Conditions

Polytetrafluoroethylene (PTFE) plays a fundamental role in proton exchange membrane fuel cells (PEMFCs) as it enhances the structural integrity of the gas diffusion and microporous layers and, more importantly, helps with water management. However, it is also vulnerable to different kinds of degradation under PEMFC o...

Rashen Lou Omongos, I. Minami, András Vernes et al. · 0 citations
Open access Aug 2026

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

A diffusion model‐based framework is established as a viable means to generate commercially relevant electrode data, intended for computational optimization and design of next‐generation energy devices.

Victor Ramirez‐Camacho, Siwar Ben Hadj Ali, Maxence Desnoyers et al. · 0 citations
Open access Jul 2026

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

A novel deep learning surrogate pipeline based on the Swin3D Transformer is introduced to predict spatiotemporal discharge dynamics directly from volumetric data, providing a scalable and efficient framework for high-throughput battery design and optimization.

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

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.