Regensburg 2025 – scientific programme

Parts | Days | Selection | Search | Updates | Downloads | Help

MM: Fachverband Metall- und Materialphysik

MM 3: Data-driven Materials Science: Big Data and Worksflows

MM 3.8: Talk

Monday, March 17, 2025, 12:15–12:30, H10

Autonomous optimization of coin-cell batteries and thin-film growth — •Edan Bainglass^1,6, Peter Kraus^2,5, Francisco Ramirez^3,6, Enea Svaluto-Ferro², Loris Ercole^3,6, Benjamin Kunz², Sebastiaan Huber^3,6, Nukorn Plainpan², Nikita Shepelin¹, Nicola Marzari^1,3,6, Corsin Battaglia^2,3,4, and Giovanni Pizzi^1,3,6 — ¹PSI, Villigen, Switzerland — ²Empa, Dübendorf, Switzerland — ³EPFL, Lausanne, Switzerland — ⁴ETH Zurich, Zurich, Switzerland — ⁵TUB, Berlin, Germany — ⁶MARVEL, Switzerland

Advancements in materials science are increasingly driven by the integration of automation of both experiments and simulations, machine learning, and robust data management frameworks. In this talk, we discuss the integration of experimental systems with the AiiDA [1] workflow management system, both battery coin cell assembly and cycling [2], and for thin film growth by pulsed laser deposition (PLD). We discuss the ongoing integration of these platforms with the FINALES [3] fast intention-agnostic learning server towards fully autonomous optimization of battery end-of-life (EOL) performance. We also discuss preliminary results demonstrating the feasibility of autonomously optimizing the layer-by-layer thin-film growth with PLD. These case studies demonstrate the potential of automated workflows to accelerate the discovery and optimization of functional materials.

[1] S. P. Huber et al., Sci. data 7, 300 (2020)

[2] P. Kraus et al., J. Mat. Chem. A 12, 10773 (2024)

[3] M. Vogler et al. Adv. Ener. Mat. 2403263 (2024)

Keywords: AiiDA; Workflows; Automation; Optimization; Materials