Towards an autonomous robotic battery materials research platform powered by automated workflow and ontologized FAIR data management

Graham Kimbell^a, Enea Svaluto-Ferro^a, Nukorn Plainpan^a, Benjamin Kunz^a, Maximilian Becker^a, David Reber^a, Ruben-Simon Kühnel^a, Peter Kraus^a, Edan Bainglass^b, Loris Ercole^c, Fancisco Ramirez^c, Giovanni Pizzi^b^,^c, Nicola Marzari^b^,^c, Yuhui Hou^e, Stefano Di Leone^e, Andrew Paterson^e, Dominique Sauter^e, Emmanouil Tzirakis^e, Jos de Keijzer^e, Amira Abou-Hamdan^e, Michael Schneider^e, Corsin Battaglia^a^,^c^,^d

^aEmpa, Swiss Federal Laboratories of Materials Science and Technology, Switzerland

^bPaul Scherrer Institut (PSI), Forschungsstrasse 111, Villigen, Switzerland

^cEcole Polytechnique Fédérale de Lausanne (EPFL)

^dETH Zurich, Department of Information Technology and Electrical Engineering, Switzerland

^eChemspeed Technologies AG, Switzerland

Materials for Sustainable Development Conference (MATSUS)
Proceedings of MATSUS Fall 2024 Conference (MATSUSFall24)

#AMADISTA - Accelerated Materials Discovery Through Automation and Machine Learning

Lausanne, Switzerland, 2024 November 12th - 15th

Organizers: Philippe Schwaller, Tobias Stubhan and Christian Wolff

Oral, Graham Kimbell, presentation 196
DOI: https://doi.org/10.29363/nanoge.matsusfall.2024.196
Publication date: 28th August 2024

Automation in experimental battery research is limited, and cell assembly and cycling often require labor-intensive steps. Additionally, the outcomes of lab-level battery research are not always reproducible and are often dependent on the skill of the researchers. Extending lab automation improves reproducibility, accelerates experiments, and frees experimentalists from repetitive tasks, providing more time for creativity.

In a joint collaborative effort, the Swiss company Chemspeed Technologies and Empa developed and validated an automated coin cell assembly robot integrated into an argon glove box. The robot can assemble 32 coin cells per batch, with anode/cathode capacity balancing fully automated to a precision of 0.01 mg. It is capable of formulating complex mixtures of liquid electrolytes, which are then dispensed with a precision of 1 µL. Cells are then cycled on a 256-channel potentiostat interfaced with an open-source Python package developed within the Battery2030+ BIG-MAP Aurora project¹. Each cell can be traced and monitored as a digital twin within the open-source workflow management platform AiiDA, developed at EPFL/PSI². The data generated will be ontologized and made FAIR (findable, accessible, interoperable, reusable) using the BattINFO ontology, adhering to principles that facilitate data sharing and reuse.

We present the first results from robotic cell assembly and cycling, demonstrating the power of the Aurora platform in accelerating battery materials research.

References:

[1] P. Kraus, N. Vetsch, C. Battaglia, J. Open Source Software, 2022, 7, 4166

[2] P. Kraus, E. Bainglass, F. F. Ramirez, E. Svaluto-Ferro, L. Ercole, B. Kunz, S. P. Huuber, N. Plainpan, N. Marzari, C. Battaglia, G. Pizzi, J. Materials Chemistry A, 2024, 12, 10773

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