The optimization of materials for specific purposes frequently requires systematic studies involving the variation of numerous parameters. An example is the optimization of liquid battery electrolytes with typically complex compositions to reach high ionic conductivity and high performance in full cell setups. To reduce the human labor and time required for such studies and accelerate optimization procedures in the field of battery research and beyond, a team of partners in the BIG-MAP project joint forces and implemented the Fast INtention-Agnostic LEarning Server (FINALES). This software framework was used to build and demonstrate two Materials Acceleration Platforms (MAPs) [1,2] both operating fully autonomously and including partners from different European countries and institutions working experimentally and computationally. The demonstrated MAPs differ in their maturity and optimization tasks but both deploy experimental and computational methods complementing each other.
This talk will outline the development of FINALES from its first demonstration to the latest more elaborate MAP. It will further show the results obtained from the autonomously conducted research campaigns with respect to the correlation between the electrolyte formulation and ionic conductivity as well as the findings regarding the end-of-life of coin cells. The talk will conclude with the lessons learned from the two development stages concerning the concept of distributed MAPs and its applicability in the field of battery research.
[1] M. Vogler, J. Busk, H. Hajiyani, P. B. Jørgensen, N. Safaei, I. E. Castelli, F. F. Ramirez, J. Carlsson, G. Pizzi, S. Clark, F. Hanke, A. Bhowmik, H. S. Stein. Brokering between tenants for an international materials acceleration platform. Matter 6, 2647-2665 (2023). https://doi.org/10.1016/j.matt.2023.07.016
[2] M. Vogler, S. K. Steensen, F. F. Ramirez, L. Merker, J. Busk, J. M. Carlsson, L. H. Rieger, B. Zhang, F. Liot, G. Pizzi, F. Hanke, E. Flores, H. Hajiyani, S. Fuchs, A. Sanin, M. Gaberšček, I. E. Castelli, S. Clark, T. Vegge, A. Bhowmik, H. S. Stein. Autonomous battery optimisation by deploying distributed experiments and simulations; ChemRxiv (2024). https://doi.org/10.26434/chemrxiv-2024-vfq1n
Laboratory for Materials Simulations LMS