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

Abstract

In this lecture, I review our recent progress within our ARTISTIC research initiative regarding the development of Physics-Informed Deep Learning frameworks to unravel the complex links between manufacturing parameters and battery electrode properties. While physics-based simulations (such as Coarse-Grained Molecular Dynamics or Discrete Element Method) offer deep insights into the formation of electrode microstructures, their high computational cost often hinders their direct use in real-time optimization loops. Here, I will demonstrate how we bridge this gap by merging AI with physics-based simulations. We utilize high-fidelity 3D physical models to generate synthetic datasets describing electrode formation steps, such as slurry drying and calendering. These datasets are then used to train Deep Learning networks that capture the underlying process-structure correlations. The resulting Deep Learning models act as ultra-fast surrogates capable of predicting the dynamics leading to the 3D microstructures of Lithium-ion battery electrodes, including the spatial location of active material, carbon-binder and pores. These predictions are experimentally validated against key metrics, such as porosity, tortuosity factor, and effective conductivities. I will illustrate how these fast surrogate models are coupled with macroscale models in order to perform multiscale modeling of manufacturing processes, and to numerical optimizers to solve inverse design problems, predicting the optimal manufacturing process parameters required to achieve specific electrode architectures. In addition to microstructural formation, I will also discuss our work on simulating electrolyte wetting phenomena by combining Lattice Boltzmann Method with AI, a critical step linking the manufactured electrodes to their electrochemical activity and aging processes. Furthermore, I will discuss the integration of experimental data from our pilot line into this workflow to ensure physical relevance, paving the way towards high-throughput screening and digital twins for battery manufacturing. Finally, I will also illustrate how we are extending these computational modeling concepts to the structural design of Redox Flow Battery electrodes. References [1] Prof. Alejandro A. Franco's group publications: https://www.modeling-electrochemistry.com/publications [2] Galvez-Aranda, D. E., Fernandez, F., & Franco, A. A. (2025). Physics-Assisted Machine Learning for the Simulation of the Slurry Drying in the Manufacturing Process of Battery Electrodes: A Hybrid Time-Dependent VGG16-DEM Model. ACS Applied Materials & Interfaces , 17 (22), 32150-32162. [3] Galvez‐Aranda, D. E., Dinh, T. L., Vijay, U., Zanotto, F. M., & Franco, A. A. (2024). Time‐Dependent Deep Learning Manufacturing Process Model for Battery Electrode Microstructure Prediction. Advanced Energy Materials , 14 (15), 2400376. [4] Vijay, U., Fernandez, F., Ben Hadj Ali, S., Asch, M., & Franco, A. A. (2025). Surrogate Modeling of Lithium‐Ion Battery Electrode Manufacturing by Combining Physics‐Based Simulation and Deep Learning. Batteries & Supercaps , e202500433. [5] Vijay, U., Galvez-Aranda, D. E., Zanotto, F. M., Le-Dinh, T., Alabdali, M., Asch, M., & Franco, A. A. (2025). A hybrid modelling approach coupling physics-based simulation and deep learning for battery electrode manufacturing simulations. Energy Storage Materials , 75 , 103883. [6] Troncoso, J. F., Zanotto, F. M., Galvez‐Aranda, D. E., Zapata Dominguez, D., Denisart, L., & Franco, A. A. (2025). The ARTISTIC battery manufacturing digitalization initiative: from fundamental research to industrialization. Batteries & Supercaps , 8 (1), e202400385. Figure 1