ERC Consolidator Grant #772873
Prof. Alejandro A. Franco

Publications

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Artificial Intelligence Applied to Battery Research: Hype or Reality?

Teo Lombardo et. al.

2021
Carbon-Binder Migration: A Three-Dimensional Evaporation Model for Lithium Ion Batteries

Teo Lombardo, Alain C. Ngandjong, Amal Belhcen, Alejandro A. Franco

2021
Machine learning 3D-resolved prediction of electrolyte infiltration in battery porous electrodes

Abbos Shodiev, Marc Duquesnoy, Oier Arcelus, Mehdi Chouchane, Jianlin Li, Alejandro A. Franco. Journal of Power Sources (2021), 511, 230384

2021
Towards a 3D-resolved model of Si/Graphite composite electrodes from manufacturing simulations

Chaoyue Liu, Oier Arcelus, Teo Lombardo, Hassan Oularbi, Alejandro A. Franco. Journal of Power Sources (2021), 512, 230486

2021
Quantitatively Designing Porous Copper Current Collectors for Lithium Metal Anodes

B. Lu, E. Olivera, J. Scharf, M. Chouchane, C. Fang, M. Ceja, L. E. Pangilinan, S. Zheng, A. Dawson, D. Cheng, W. Bao, O. Arcelus, A. A. Franco, X. Li, S. H. Tolbert, Y. S. Meng. ACS Appl. Energy Mater (2021), 4, 7, 6454–6465

2021
An Invitation to Engage with Computational Modeling: User-Friendly Tool for In Silico Battery Component Generation and Meshing

Mehdi Chouchane, Alejandro A. Franco. Batteries & Supercaps (2021)

2021
Machine Learning-Based Assessment of the Impact of the Manufacturing Process on Battery Electrode Heterogeneity

Marc Duquesnoy, Iker Boyano, Larraitz Ganborena, Pablo Cereijo, Elixabete Ayerbe, Alejandro A. Franco. Energy & AI. 100090

2021
Heterogeneous Solid Electrolyte Interphase in Graphite Electrodes Assessed by 4D‐Resolved Computational Simulations

Mehdi Chouchane Oier Arcelus Alejandro A. Franco. Batteries & Supercaps (2021), 10.1002/batt.202100030 .

2021
Escape from flatland

Alejandro A. Franco. Nat Mach Intell (2021)

2021
Fabrication of High-Quality Thin Solid-State Electrolyte Films Assisted by Machine Learning

Yu-Ting Chen, Marc Duquesnoy, Darren H. S. Tan, Jean-Marie Doux, Hedi Yang, Grayson Deysher, Phillip Ridley, Alejandro A. Franco, Ying Shirley Meng and Zheng Chen. ACS Energy Lett. (2021), 6, 4, 1639-1648

2021
How Machine Learning Will Revolutionize Electrochemical Sciences

Aashutosh Mistry, Alejandro A. Franco, Samuel J. Cooper, Scott A. Roberts, Venkatasubramanian Viswanathan. ACS Energy Lett. (2021), 6, 1422–1431

2021
Insight on Electrolyte Infiltration of Lithium Ion Battery Electrodes by Means of a New Three-Dimensional-Resolved Lattice Boltzmann Model

Abbos Shodiev, Emiliano Primo, Oier Arcelus, Mehdi Chouchane, Markus Osenberg, André Hilger, Ingo Manke, Jianlin Li, Alejandro A. Franco. Energy Storage Materials (2021), In Press, Journal Pre-proof, Available online 24 February 2021

2021
Calendering of Li(Ni0.33Mn0.33Co0.33)O2‐Based Cathodes: Analyzing the Link Between Process Parameters and Electrode Properties by Advanced Statistics

Emiliano N. Primo, Matthieu Touzin, Alejandro A. Franco. Batteries & Supercaps (2021), 4, 1–12

2021
What Can Text Mining Tell Us About Lithium‐Ion Battery Researchers’ Habits?

Hassna El‐Bousiydy, Teo Lombardo, Emiliano N. Primo, Marc Duquesnoy, Mathieu Morcrette, Patrik Johansson, Patrice Simon, Alexis Grimaud, Alejandro A. Franco. Batteries & Supercaps (2021), 4, 1–10

2021
Understanding the calendering processability of Li(Ni0.33Mn0.33Co0.33)O2-based cathodes

Emiliano N. Primo, Mehdi Chouchane, Matthieu Touzin, Patricia Vazquez, Alejandro A. Franco. Journal of Power Sources (2021), 488, 229361

2021

Publications

Artificial Intelligence Applied to Battery Research: Hype or Reality?

Carbon-Binder Migration: A Three-Dimensional Evaporation Model for Lithium Ion Batteries

Machine learning 3D-resolved prediction of electrolyte infiltration in battery porous electrodes

Towards a 3D-resolved model of Si/Graphite composite electrodes from manufacturing simulations

Quantitatively Designing Porous Copper Current Collectors for Lithium Metal Anodes

An Invitation to Engage with Computational Modeling: User-Friendly Tool for In Silico Battery Component Generation and Meshing

Machine Learning-Based Assessment of the Impact of the Manufacturing Process on Battery Electrode Heterogeneity

Heterogeneous Solid Electrolyte Interphase in Graphite Electrodes Assessed by 4D‐Resolved Computational Simulations

Escape from flatland

Fabrication of High-Quality Thin Solid-State Electrolyte Films Assisted by Machine Learning

How Machine Learning Will Revolutionize Electrochemical Sciences

Insight on Electrolyte Infiltration of Lithium Ion Battery Electrodes by Means of a New Three-Dimensional-Resolved Lattice Boltzmann Model

Calendering of Li(Ni0.33Mn0.33Co0.33)O2‐Based Cathodes: Analyzing the Link Between Process Parameters and Electrode Properties by Advanced Statistics

What Can Text Mining Tell Us About Lithium‐Ion Battery Researchers’ Habits?

Understanding the calendering processability of Li(Ni0.33Mn0.33Co0.33)O2-based cathodes