Authors: Isidora Cekić-Lasković | Peng Yan | Christian Wölke | Mirko Fischer | Anand Narayanan Krishnamoorthy | Diddo Diddens | Andreas Heuer | Martin Winter
Source: ECS Meeting Abstracts (article, 2025-11-24)
Field: Materials Science — Materials Chemistry

<i>(Invited)</i> Accelerated Discovery of Novel Non-Aqueous Battery Electrolytes The development of next-generation batteries requires a breakthrough in materials. High throughput experimentation (HTE) is an effective approach to characterize a large number of materials over a broad compositional space in a short time, considerably accelerating the discovery and optimization of materials and their combinations. The main goal is to parallelize automated, repetitive tasks and fully capture data in a findable, accessible, interoperable, reusable (FAIR) fashion, enabling the creation of data libraries. Each HTE facility is unique in its design and nature. To realize full benefits of HTE, careful investment in strategy, hardware and software is required. Well-designed high throughput experiments result in wealth of experimental data, acquired and processed in automated fashion, that creates the foundation for enhanced technical decision making[1]. Acquired datasets have a dual functionality: they enable identification of hit/lead material candidates, in this case electrolyte formulations for given cell chemistries and targeted applications, based on a set of pre-defined criteria (at electrolyte and lab cell level) and key parameter indicators, and serve as “feeding units” for machine learning orchestrated analysis. The integration of HTE with data-driven analysis represents a transformative step in the evolution of materials discovery. The extensive datasets acquired from HTE lays the foundation for optimized design and a comprehensive understanding of electrolytes with data-driven analysis. Through the use of machine learning algorithms, data-driven analysis enables the identification of complex relationships and patterns within the data that may not be apparent using traditional analysis methods[2]. This talk will highlight state-of-the-art achievements along with an assessment of current and future challenges as well as resulting perspectives towards accelerated development of advanced non-aqueous battery electrolytes and their interfaces/interphases in different battery chemistries. References: A. Benayad, D. Diddens, A. Heuer, A. N. Krishnamoorthy, M. Maiti, F. Le Cras, M. Legallais, F. Rahmanian, Y. Shin, H. Stein, M. Winter, C. Wölke, P. Yan, I. Cekic-Laskovic,High-Throughput Experimentation and Computational Freeway Lanes for Accelerated Battery Electrolyte and Interface Development Research , Advanced Energy Materials 2021, 2102678; DOI:10.1002/aenm.202102678 P. Yan, Mirko Fischer, H. Martin, C. Wölke, A. N. Krishnamoorthy, I. Cekic-Laskovic, D. Diddens, M. Winter, A. Heuer, Non-aqueous Battery Electrolytes: High-Throughput Experimentation and Machine Learning-Aided Optimization of Ionic Conductivity, Journal of Materials Chemistry 2024, A 12(30); DOI:10.1039/D3TA06249J Identification (biology) ; Throughput ; Software ; Key (lock) ; Battery (electricity) ; Set (abstract data type) Machine Learning in Materials Science ; Inorganic Chemistry and Materials ; Advanced Battery Materials and Technologies